1. A method for dynamically generating a data model for forecasting data related to an event, method comprising: receiving, by a processor of a data modelling system, a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data; determining a reference statistical summary value of the plurality of historic dataset; classifying dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables; determining a specific statistical summary value of each of the classified plurality of dataset groups; and generating a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

2. The method as claimed in claim 1, wherein classifying dynamically the plurality of historic dataset comprising the steps of: selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting; determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

3. The method as claimed in claim 1, wherein generating the data model comprises the steps of: comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups; determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial; estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination; determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and generating the data model based on the significance matrix thus determined.

4. The method as claimed in claim 1, further comprising determining a future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

5. The method as claimed in claim 1, further comprising updating dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data.

6. A system for dynamically generating a data model for forecasting data related to an event, system comprising: a processor; a historic data (HD) database coupled with the processor and configured to store a plurality of historic dataset comprising data corresponding to one or more first and second variables associated with forecasting data; and a memory disposed in communication with the processor and storing processor-executable instructions, the instructions comprising instructions to: receive a plurality of historic dataset from the HD database; determine a reference statistical summary value of the plurality of historic dataset; classify dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables; determine a specific statistical summary value of each of the classified plurality of dataset groups; and generate a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

7. The system as claimed in claim 6, wherein the processor is configured to classify dynamically the plurality of historic dataset by performing the steps of: selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting; determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

8. The system as claimed in claim 6, wherein the processor is configured to generate the data model by the steps of: comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups; determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial; estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination; determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and generating the data model based on the significance matrix thus determined.

9. The system as claimed in claim 6, wherein the processor is further configured to determine the future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

10. The system as claimed in claim 6, wherein the processor is further configured to update dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data.

11. A non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor cause a system to perform acts of: receiving a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data; determining a reference statistical summary value of the plurality of historic dataset; classifying dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables; determining a specific statistical summary value of each of the classified plurality of dataset groups; and generating a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

12. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to classify dynamically the plurality of historic dataset by the steps of: selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting; determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

13. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to generate the data model by the steps of: comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups; determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial; estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination; determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and generating the data model based on the significance matrix thus determined.

14. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to determine the future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

15. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to update dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data. ,TagSPECI:FIELD OF THE DISCLOSURE The present subject matter is related, in general to data forecasting, and more particularly, but not exclusively to method and system for dynamically generating a data model for forecasting data related to an event. BACKGROUND Generally, forecasting data for products and/or service are paramount concerns for many organizations. Forecasting predicts future demand for a product and/or service based on historical and judgmental data in various ways. Demand forecast is a key parameter for various business activities, particularly inventory control and replenishment and/or effectively managing a service and hence it significantly contributes to the organization’s productivity and profit. However, there are errors in forecasting natural/human phenomenon due to inability to comprehensively consider all factors affecting the said phenomenon or event and due to faulty sensor or interface error. Conventional forecasting methods correct the errors by gathering historic error data and generating its probability distribution based on which the forecast value is modified. However, these conventional methods reduce the errors marginally. Therefore, there is a need for method and system for dynamically generating a data model for forecasting that reduces the errors and overcoming the disadvantages and limitations of the existing forecasting systems. SUMMARY One or more shortcomings of the prior art are overcome and additional advantages are provided through the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure. Accordingly, the present disclosure relates to a method of dynamically generating a data model for forecasting data related to an event. The method comprising the steps of receiving a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data and determining a reference statistical summary value of the plurality of historic dataset. The method further comprises the steps of classifying dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determining a specific statistical summary value of each of the classified plurality of dataset groups. Upon determining, the data model for the classified plurality of dataset groups is generated based on the comparison of the reference statistical summary value and the specific statistical summary value. Further, the present disclosure relates to a system for dynamically generating a data model for forecasting data related to an event. The system comprises a historic data (HD) database coupled with the processor and configured to store a plurality of historic dataset comprising data corresponding to one or more first and second variables associated with forecasting data. The system further comprises a processor and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to receive a plurality of historic dataset from the HD database. The processor is furthermore configured to determine a reference statistical summary value of the plurality of historic dataset. The processor further classifies dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determines a specific statistical summary value of each of the classified plurality of dataset groups. Upon dynamically classifying the plurality of historic dataset, the processor generates a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value. Furthermore, the present disclosure relates to a non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor causes a system to perform the act of receiving a plurality of historic dataset from a historic data (HD) database, the data set comprises data corresponding to one or more first and second variables associated with forecasting data. Further, the instructions cause the processor to determining a reference statistical summary value of the plurality of historic dataset. Furthermore, the instructions cause the processor to classify dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determine a specific statistical summary value of each of the classified plurality of dataset groups. Upon dynamically classifying the plurality of historic dataset, the instructions caused the processor to generate a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value. The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which: Figure 1 illustrates an architecture diagram of an exemplary system for dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure; Figure 2 illustrates an exemplary block diagram of a data modelling system of Figure 1 in accordance with some embodiments of the present disclosure; Figure 3 illustrates a flowchart of an exemplary method of dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure; Figure 4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown. DETAILED DESCRIPTION In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure. The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus. The present disclosure relates to a method and a system for dynamically generating a data model for forecasting data related to an event. In one embodiment, the method receives historic dataset stored in a historic data database and determines a reference statistical summary value of the historic dataset. The method further classifies the historic dataset into a plurality of dataset groups and determines a specific statistical summary value of the plurality of dataset groups. Furthermore, the method generates a data model for the classified plurality of dataset groups based on the reference statistical summary and specific statistical summary values. The data model thus generated enables determination of the future forecasted value of data with reduced errors. Thus the proposed data model minimizes the errors in forecasting data, thereby improving the economic status and operational efficiency of the organization. In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. Figure 1 illustrates an architecture diagram of an exemplary system for dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure; As shown in Figure 1, the exemplary system 100 comprises one or more components configured to dynamically generate the data model for forecasting data related to an event. In one embodiment, the exemplary system 100 comprises a data modelling system 102, a plurality of sensors 104-1, 104-2,…104-N (collectively referred to as sensors 104) and a historic data (HD) database 106 connected via a communication network 108. The data modelling system 102 dynamically generates the data model based on data captured by sensors 104 and stored in the HD database 106. In one embodiment, the sensors 104 and the HD database 106 may be integrated within the data modelling system 102. In another embodiment, the sensors 104 and the HD database 106 may be implemented independent of the data modelling system 102. The sensors 104 may be for example, the data sensors configured to receive data related to an event. The data may include data associated with a plurality of variables associated with the past historic events. The data may be stored in the HD database and hereinafter referred to as plurality of historic data. The plurality of historic data is processed by the data modelling system 102 to generate the data model dynamically in real time. In one embodiment, the data modelling system 102 comprises a central processing unit (“CPU” or “processor”) 110, a memory 112, a statistical summary value analyzer (SSVA) 114, a dataset classifier 116 and a data model generator 118. The data modelling system 102 may be a typical data modelling system as illustrated in Figure 2. The data modelling system 102 comprises the processor 110, the memory 112 and an I/O interface 202. The I/O interface 106 is coupled with the processor 110 and an I/O device. The I/O device is configured to receive inputs via the I/O interface 106 and transmit outputs for displaying in the I/O device via the I/O interface 106. The data modelling system 102 further comprises data 204 and modules 206. In one implementation, the data 204 and the modules 206 may be stored within the memory 104. In one example, the data 204 may include historic dataset 208, reference statistical summary value 210, specific statistical summary value 212, significance matrix 214, plurality of significance interval 216 and other data 218. In one embodiment, the data 204 may be stored in the memory 104 in the form of various data structures. Additionally, the aforementioned data can be organized using data models, such as relational or hierarchical data models. The other data 218 may be also referred to as reference repository for storing recommended implementation approaches as reference data. The other data 218 may also store data, including temporary data and temporary files, generated by the modules 206 for performing the various functions of the system 102. The modules 206 may include, for example, the statistical summary value analyzer 114, the dataset classifier 116, the data model generator 118, a data updating module 220, and a future forecast value determination (FFVD) module 222. The modules 206 may also comprise other modules 224 to perform various miscellaneous functionalities of the data modelling system 102. It will be appreciated that such aforementioned modules may be represented as a single module or a combination of different modules. The modules 206 may be implemented in the form of software, hardware and/or firmware. The data modelling system 102 receives the plurality of historic dataset 208 from the HD database 106. The plurality of historic dataset 208 comprises one or more data corresponding to one or more response or dependent variables (hereinafter referred to as first variables) and data associated with one or more suspected predictor or independent variables (hereinafter referred to as second variables). The SSVA 114 determines the reference statistical summary value 210 of the plurality of historic dataset 208 using known techniques. In one example, the SSVA 114 determines the reference mean value of the plurality of historic dataset based on data related to the one or more first variables. Upon determining the reference statistical summary value 210, a plurality of dataset groups is generated using classification techniques. In one embodiment, the dataset classifier 116 dynamically classifies the plurality of historic dataset 208 based on data related to the one or more second variables. The dataset classifier 116 selects the one or more second variables and determines boundary values of the one or more selected second variables. Based on the boundary values, the dataset classifier 116 repeatedly determines a plurality of discrete interval values and a step-size corresponding to the plurality of discrete interval values. In another embodiment, a user may manually provide the boundary values, the plurality of discrete interval values and the corresponding step-sizes. Upon determination, the dataset classifier 116 generates the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size. For each of the plurality of classified dataset groups, the SSVA 114 determines the specific statistical summary value 212 using known techniques. In one example, the SSVA 114 determines the specific mean value of each of the plurality of classified dataset groups based on data related to the one or more second variables. Upon determining the specific statistical summary value 212, the data model is generated based on the reference statistical summary value 210 and the specific statistical summary value 212. In one embodiment, the data model generator 118 compares the reference statistical summary value 210 and the specific statistical summary value 212 and determines a statistical significance factor (k) based on the comparison. The significance factor (k) indicates as to whether there exists a substantial difference or not between the reference statistical summary value 210 and the specific statistical summary value 212. The value of k may be for example, between 0 and 1. If the data model generator 118 determines that there exists a substantial difference, then the significance factor (k) is set equals to 1 and the specific statistical summary value 212 is determined as the forecasted statistical summary value. On the other hand, if the data model generator 118 determines that there exists no substantial difference, and then the significance factor (k) is set equals to 0 and the reference statistical summary value 212 is determined as the forecasted statistical summary value. Based on the forecasted statistical summary value thus determined, the data model generator 118 generates a significance matrix of the one or more second variables. In one embodiment, the data model generator 118 generates the significance matrix based on the determined significance factor (k). The significance matrix may be a matrix of the significance factor (k) mapped against the plurality of discrete intervals (alternatively referred to as significance intervals) of one or more second variables. Based on the significance matrix, the data model generator 118 determines the data model by mapping the corresponding forecasted statistical summary value of the one or more second variables. Upon generating the data model, the future forecasted value of data is determined. In one embodiment, the FFVD module 220 determines the future forecasted value of data based on the historically determined forecasted data value stored in the HD database 106 and the data model thus generated. The data updating module 222 dynamically updates the future forecasted value of data, the generated data model and actual value corresponding to the forecast in the HD database 106 for further forecasting of data. Thus, the present disclosure dynamically generates a data model for forecasting data related to the event that reduces the errors that arise during forecasting by considering all factors or variables and improve the efficiency of the data forecasting. Figure 3 illustrates a flowchart of an exemplary method of dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure; As illustrated in Figure 3, the method 300 comprises one or more blocks implemented by the processor 110 for dynamically generating a data model for forecasting data related to the event. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types. The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. At block 302, receive historic dataset. In one embodiment, the data modelling system 102 receives the plurality of historic dataset 208 from the HD database 106. The plurality of historic dataset 208 comprises one or more data corresponding to the one or more first variables and data associated with the one or more second variables. Let us consider an illustrating scenario, where the data modelling system 102 receives historic dataset of for example, error data related to forecasting of power consumption data obtained at particular time at a particular location. The data modelling system 102 aims at error modeling to reduce the errors thus obtained during the forecasting of power consumption data so as to monitor the power consumption at different locations at different time periods and reduce the unused energy and thereby improve the efficiency of power grid operation. Actual data related to energy consumption captured during past time at different locations is stored in the HD database 106. Further, corresponding forecasted data and error data obtained due to the difference between the forecasted and actual data are stored in the HD database 106. Thus, the plurality of historic dataset comprises error data as data of the one or more first variables (y) and temporal, spatial/geographical data as data of the one or more second variables (a b). In one example, let us consider that the HD database 106 may comprise data from an electric utility i.e., 24 hours error data of 1st to 3rd April 2014 and the data modelling system 102 forecast the load of 8th April 2014 at 2AM at location 1. At block 304, determine reference statistical summary value. In one embodiment, the SSVA 114 determines the reference statistical summary value 210 of the plurality of historic dataset 208. In one example, the SSVA 114 determines the reference mean value (µr) of the plurality of historic dataset based on data related to the one or more first variables. In one aspect, the probability distribution of the error data is graphically determined along the time. As illustrated, the reference mean value µr of the historic error data is determined i.e., µr = 389 MW. At block 306, classify the historic dataset into dataset groups. In one embodiment, the dataset classifier 116 dynamically classifies the plurality of historic dataset 208 based on data related to the one or more second variables. The dataset classifier 116 selects the one or more second variables and determines boundary values of the one or more selected second variables. Based on the boundary values, the dataset classifier 116 repeatedly determines a plurality of discrete interval values and a step-size corresponding to the plurality of discrete interval values. As illustrated, the dataset classifier 116 generates the plurality of dataset groups i.e., the error data at 2 AM at location 1 for the three days. At block 308, determine specific statistical summary value of each dataset group. For each of the plurality of classified dataset groups, the SSVA 114 determines the specific statistical summary value 212 using known techniques. In one example, the SSVA 114 determines the specific mean value (µ1) of each of the plurality of classified dataset groups based on data related to the one or more second variables. As per the illustration, the SSVA 114 determines the specific mean value µ1 based on the error data at 2AM at location 1 for the three days i.e., µ1 = 292 MW. At block 310, generate a data model. In one embodiment, the data model generator 118 compares the reference statistical summary value 210 and the specific statistical summary value 212 and determines a statistical significance factor (k) based on the comparison. The significance factor (k) indicates as to whether there exists a substantial difference or not between the reference statistical summary value 210 and the specific statistical summary value 212. The value of k may be for example, between 0 and 1. If the data model generator 118 determines that there exists a substantial difference, then the significance factor (k) is set equals to 1 and the specific statistical summary value 212 is determined as the forecasted statistical summary value. On the other hand, if the data model generator 118 determines that there exists no substantial difference, and then the significance factor (k) is set equals to 0 and the reference statistical summary value 212 is determined as the forecasted statistical summary value. As illustrated, the data model generator 118 determines a significant difference between µr and µ1, therefore, the value of significance factor k is set to 1. Based on the forecasted statistical summary value thus determined, the data model generator 118 generates a significance matrix of the one or more second variables. In one embodiment, the data model generator 118 generates the significance matrix based on the determined significance factor (k). The significance matrix may be a matrix of the significance factor (k) mapped against the plurality of discrete intervals (alternatively referred to as significance intervals) of one or more second variables. For example, the second variable ‘a’ may be the temporal variable for example, time of the day say 1AM or 2 AM and ‘b’ may be spatial variable, for example geographic location 1 and geographic location 2. a 1 AM 2 AM b Location 1 0 1 Location 2 0 1 At the location 1, it may be noted that the temporal variable at 2AM has high significance interval in the data model thus generated. Based on the significance matrix, the data model generator 118 determines the data model by mapping the corresponding the forecasted statistical summary value of the one or more second variables as illustrated below in equation (1). y1 = µr * (1-k) + µ1 * k …………………………………………….. (1) Upon generating the data model, the future forecasted value of data is determined. In one embodiment, the FFVD module 220 determines the future forecasted value of data based on the historically determined forecasted data value stored in the HD database 106 and the data model of error thus generated. An illustration of comparison of results of conventional and proposed data model is shown below: Conventional data model Exemplary data model Error model y= µr y1 = µr * (1-k) + µ1 * k Error model output 389 MW 292 MW (since k=1) New Forecast value 13306 + 389 = 13695 MW 13306 + 292 = 13598 MW Actual load 13595 13595 New Error 100 MW 3 MW The data updating module 22 then dynamically updates the future forecasted value of data, the generated data model in the HD database 106 for further forecasting of data. Figure 4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. Variations of computer system 401 may be used for implementing all the computing systems that may be utilized to implement the features of the present disclosure. Computer system 401 may comprise a central processing unit (“CPU” or “processor”) 402. Processor 402 may comprise at least one data processor for executing program components for executing user- or system-generated requests. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor 402 may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor 402 may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc. Processor 402 may be disposed in communication with one or more input/output (I/O) devices via I/O interface 403. The I/O interface 403 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc. Using the I/O interface 403, the computer system 401 may communicate with one or more I/O devices. For example, the input device 404 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, visors, etc. Output device 405 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, or the like), audio speaker, etc. In some embodiments, a transceiver 406 may be disposed in connection with the processor 402. The transceiver may facilitate various types of wireless transmission or reception. For example, the transceiver may include an antenna operatively connected to a transceiver chip (e.g., Texas Instruments WiLink WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc. In some embodiments, the processor 402 may be disposed in communication with a communication network 408 via a network interface 407. The network interface 407 may communicate with the communication network 408. The network interface 407 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/40/400 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 408 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 407 and the communication network 408, the computer system 401 may communicate with devices 409, 410, and 411. These devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system 401 may itself embody one or more of these devices. In some embodiments, the processor 402 may be disposed in communication with one or more memory devices (e.g., RAM 413, ROM 414, etc.) via a storage interface 412. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc. The memory 415 may store a collection of program or database components, including, without limitation, an operating system 416, user interface application 417, web browser 418, mail server 419, mail client 420, user/application data 421 (e.g., any data variables or data records discussed in this disclosure), etc. The operating system 416 may facilitate resource management and operation of the computer system 401. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like. User interface 417 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 401, such as cursors, icons, check boxes, menus, scrollers, windows, widgets, etc. Graphical user interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, Javascript, AJAX, HTML, Adobe Flash, etc.), or the like. In some embodiments, the computer system 401 may implement a web browser 418 stored program component. The web browser may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, application programming interfaces (APIs), etc. In some embodiments, the computer system 301 may implement a mail server 419 stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as internet message access protocol (IMAP), messaging application programming interface (MAPI), Microsoft Exchange, post office protocol (POP), simple mail transfer protocol (SMTP), or the like. In some embodiments, the computer system 401 may implement a mail client 420 stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc. In some embodiments, computer system 401 may store user/application data 421, such as the data, variables, records, etc. as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object-oriented databases (e.g., using ObjectStore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of the any computer or database component may be combined, consolidated, or distributed in any working combination. As described above, the modules 206, amongst other things, include routines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The modules 206 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulate signals based on operational instructions. Further, the modules 206 can be implemented by one or more hardware components, by computer-readable instructions executed by a processing unit, or by a combination thereof. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media. It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.

CLIAMS:We Claim:

1. A method for dynamically generating a data model for forecasting data related to an event, method comprising:
receiving, by a processor of a data modelling system, a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data;
determining a reference statistical summary value of the plurality of historic dataset;
classifying dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables;
determining a specific statistical summary value of each of the classified plurality of dataset groups; and
generating a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

2. The method as claimed in claim 1, wherein classifying dynamically the plurality of historic dataset comprising the steps of:
selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting;
determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and
generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

3. The method as claimed in claim 1, wherein generating the data model comprises the steps of:
comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups;
determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial;
estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination;
determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and
generating the data model based on the significance matrix thus determined.

4. The method as claimed in claim 1, further comprising determining a future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

5. The method as claimed in claim 1, further comprising updating dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data.

6. A system for dynamically generating a data model for forecasting data related to an event, system comprising:
a processor;
a historic data (HD) database coupled with the processor and configured to store a plurality of historic dataset comprising data corresponding to one or more first and second variables associated with forecasting data; and
a memory disposed in communication with the processor and storing processor-executable instructions, the instructions comprising instructions to:
receive a plurality of historic dataset from the HD database;
determine a reference statistical summary value of the plurality of historic dataset;
classify dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables;
determine a specific statistical summary value of each of the classified plurality of dataset groups; and
generate a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

7. The system as claimed in claim 6, wherein the processor is configured to classify dynamically the plurality of historic dataset by performing the steps of:
selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting;
determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and
generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

8. The system as claimed in claim 6, wherein the processor is configured to generate the data model by the steps of:
comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups;
determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial;
estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination;
determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and
generating the data model based on the significance matrix thus determined.

9. The system as claimed in claim 6, wherein the processor is further configured to determine the future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

10. The system as claimed in claim 6, wherein the processor is further configured to update dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data.

11. A non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor cause a system to perform acts of:
receiving a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data;
determining a reference statistical summary value of the plurality of historic dataset;
classifying dynamically the plurality of historic dataset into a plurality of dataset groups based on the one or more second variables;
determining a specific statistical summary value of each of the classified plurality of dataset groups; and
generating a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

12. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to classify dynamically the plurality of historic dataset by the steps of:
selecting the one or more second variables of the plurality of historic dataset and determining boundary values of the one or more second variables upon selecting;
determining in recurrence, a plurality of discrete interval values of the one or more second variables and a step-size corresponding to the plurality of discrete interval values thus determined; and
generating the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size thus determined.

13. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to generate the data model by the steps of:
comparing the reference statistical summary value of the plurality of historic dataset and the specific statistical summary value of the classified plurality of dataset groups;
determining whether the difference between the reference statistical summary value and the specific statistical summary value is substantial;
estimating one of the reference statistical summary value and the specific statistical summary value as a forecasted statistical summary value based on the determination;
determining a significance matrix of the one or more second variables based on the forecasted statistical summary value thus estimated; and
generating the data model based on the significance matrix thus determined.

14. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to determine the future forecasted value of data based on the generated data model and a historically determined forecasted data value stored in the HD database.

15. The medium as claimed in claim 11, wherein the instructions, on execution, further cause the at least one processor to update dynamically the future forecasted value of data, the generated data model in the HD database for further forecasting of data.
,TagSPECI:FIELD OF THE DISCLOSURE
The present subject matter is related, in general to data forecasting, and more particularly, but not exclusively to method and system for dynamically generating a data model for forecasting data related to an event.
BACKGROUND
Generally, forecasting data for products and/or service are paramount concerns for many organizations. Forecasting predicts future demand for a product and/or service based on historical and judgmental data in various ways. Demand forecast is a key parameter for various business activities, particularly inventory control and replenishment and/or effectively managing a service and hence it significantly contributes to the organization’s productivity and profit. However, there are errors in forecasting natural/human phenomenon due to inability to comprehensively consider all factors affecting the said phenomenon or event and due to faulty sensor or interface error. Conventional forecasting methods correct the errors by gathering historic error data and generating its probability distribution based on which the forecast value is modified. However, these conventional methods reduce the errors marginally.
Therefore, there is a need for method and system for dynamically generating a data model for forecasting that reduces the errors and overcoming the disadvantages and limitations of the existing forecasting systems.
SUMMARY

One or more shortcomings of the prior art are overcome and additional advantages are provided through the present disclosure. Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.
Accordingly, the present disclosure relates to a method of dynamically generating a data model for forecasting data related to an event. The method comprising the steps of receiving a plurality of historic dataset from a historic data (HD) database, the dataset comprises data corresponding to one or more first and second variables associated with forecasting data and determining a reference statistical summary value of the plurality of historic dataset. The method further comprises the steps of classifying dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determining a specific statistical summary value of each of the classified plurality of dataset groups. Upon determining, the data model for the classified plurality of dataset groups is generated based on the comparison of the reference statistical summary value and the specific statistical summary value.
Further, the present disclosure relates to a system for dynamically generating a data model for forecasting data related to an event. The system comprises a historic data (HD) database coupled with the processor and configured to store a plurality of historic dataset comprising data corresponding to one or more first and second variables associated with forecasting data. The system further comprises a processor and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to receive a plurality of historic dataset from the HD database. The processor is furthermore configured to determine a reference statistical summary value of the plurality of historic dataset. The processor further classifies dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determines a specific statistical summary value of each of the classified plurality of dataset groups. Upon dynamically classifying the plurality of historic dataset, the processor generates a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.
Furthermore, the present disclosure relates to a non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor causes a system to perform the act of receiving a plurality of historic dataset from a historic data (HD) database, the data set comprises data corresponding to one or more first and second variables associated with forecasting data. Further, the instructions cause the processor to determining a reference statistical summary value of the plurality of historic dataset. Furthermore, the instructions cause the processor to classify dynamically the plurality of historic data set into a plurality of dataset groups based on the one or more second variables and determine a specific statistical summary value of each of the classified plurality of dataset groups. Upon dynamically classifying the plurality of historic dataset, the instructions caused the processor to generate a data model for the classified plurality of dataset groups based on the comparison of the reference statistical summary value and the specific statistical summary value.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.
BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:
Figure 1 illustrates an architecture diagram of an exemplary system for dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure;
Figure 2 illustrates an exemplary block diagram of a data modelling system of Figure 1 in accordance with some embodiments of the present disclosure;
Figure 3 illustrates a flowchart of an exemplary method of dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure;
Figure 4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
The present disclosure relates to a method and a system for dynamically generating a data model for forecasting data related to an event. In one embodiment, the method receives historic dataset stored in a historic data database and determines a reference statistical summary value of the historic dataset. The method further classifies the historic dataset into a plurality of dataset groups and determines a specific statistical summary value of the plurality of dataset groups. Furthermore, the method generates a data model for the classified plurality of dataset groups based on the reference statistical summary and specific statistical summary values. The data model thus generated enables determination of the future forecasted value of data with reduced errors. Thus the proposed data model minimizes the errors in forecasting data, thereby improving the economic status and operational efficiency of the organization.
In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.
Figure 1 illustrates an architecture diagram of an exemplary system for dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure;
As shown in Figure 1, the exemplary system 100 comprises one or more components configured to dynamically generate the data model for forecasting data related to an event. In one embodiment, the exemplary system 100 comprises a data modelling system 102, a plurality of sensors 104-1, 104-2,…104-N (collectively referred to as sensors 104) and a historic data (HD) database 106 connected via a communication network 108. The data modelling system 102 dynamically generates the data model based on data captured by sensors 104 and stored in the HD database 106. In one embodiment, the sensors 104 and the HD database 106 may be integrated within the data modelling system 102. In another embodiment, the sensors 104 and the HD database 106 may be implemented independent of the data modelling system 102.
The sensors 104 may be for example, the data sensors configured to receive data related to an event. The data may include data associated with a plurality of variables associated with the past historic events. The data may be stored in the HD database and hereinafter referred to as plurality of historic data. The plurality of historic data is processed by the data modelling system 102 to generate the data model dynamically in real time.
In one embodiment, the data modelling system 102 comprises a central processing unit (“CPU” or “processor”) 110, a memory 112, a statistical summary value analyzer (SSVA) 114, a dataset classifier 116 and a data model generator 118. The data modelling system 102 may be a typical data modelling system as illustrated in Figure 2. The data modelling system 102 comprises the processor 110, the memory 112 and an I/O interface 202. The I/O interface 106 is coupled with the processor 110 and an I/O device. The I/O device is configured to receive inputs via the I/O interface 106 and transmit outputs for displaying in the I/O device via the I/O interface 106.
The data modelling system 102 further comprises data 204 and modules 206. In one implementation, the data 204 and the modules 206 may be stored within the memory 104. In one example, the data 204 may include historic dataset 208, reference statistical summary value 210, specific statistical summary value 212, significance matrix 214, plurality of significance interval 216 and other data 218. In one embodiment, the data 204 may be stored in the memory 104 in the form of various data structures. Additionally, the aforementioned data can be organized using data models, such as relational or hierarchical data models. The other data 218 may be also referred to as reference repository for storing recommended implementation approaches as reference data. The other data 218 may also store data, including temporary data and temporary files, generated by the modules 206 for performing the various functions of the system 102.
The modules 206 may include, for example, the statistical summary value analyzer 114, the dataset classifier 116, the data model generator 118, a data updating module 220, and a future forecast value determination (FFVD) module 222. The modules 206 may also comprise other modules 224 to perform various miscellaneous functionalities of the data modelling system 102. It will be appreciated that such aforementioned modules may be represented as a single module or a combination of different modules. The modules 206 may be implemented in the form of software, hardware and/or firmware.
The data modelling system 102 receives the plurality of historic dataset 208 from the HD database 106. The plurality of historic dataset 208 comprises one or more data corresponding to one or more response or dependent variables (hereinafter referred to as first variables) and data associated with one or more suspected predictor or independent variables (hereinafter referred to as second variables). The SSVA 114 determines the reference statistical summary value 210 of the plurality of historic dataset 208 using known techniques. In one example, the SSVA 114 determines the reference mean value of the plurality of historic dataset based on data related to the one or more first variables. Upon determining the reference statistical summary value 210, a plurality of dataset groups is generated using classification techniques.
In one embodiment, the dataset classifier 116 dynamically classifies the plurality of historic dataset 208 based on data related to the one or more second variables. The dataset classifier 116 selects the one or more second variables and determines boundary values of the one or more selected second variables. Based on the boundary values, the dataset classifier 116 repeatedly determines a plurality of discrete interval values and a step-size corresponding to the plurality of discrete interval values. In another embodiment, a user may manually provide the boundary values, the plurality of discrete interval values and the corresponding step-sizes. Upon determination, the dataset classifier 116 generates the plurality of dataset groups of the one or more second variables based on the plurality of discrete interval values and step-size. For each of the plurality of classified dataset groups, the SSVA 114 determines the specific statistical summary value 212 using known techniques. In one example, the SSVA 114 determines the specific mean value of each of the plurality of classified dataset groups based on data related to the one or more second variables. Upon determining the specific statistical summary value 212, the data model is generated based on the reference statistical summary value 210 and the specific statistical summary value 212.
In one embodiment, the data model generator 118 compares the reference statistical summary value 210 and the specific statistical summary value 212 and determines a statistical significance factor (k) based on the comparison. The significance factor (k) indicates as to whether there exists a substantial difference or not between the reference statistical summary value 210 and the specific statistical summary value 212. The value of k may be for example, between 0 and 1. If the data model generator 118 determines that there exists a substantial difference, then the significance factor (k) is set equals to 1 and the specific statistical summary value 212 is determined as the forecasted statistical summary value. On the other hand, if the data model generator 118 determines that there exists no substantial difference, and then the significance factor (k) is set equals to 0 and the reference statistical summary value 212 is determined as the forecasted statistical summary value. Based on the forecasted statistical summary value thus determined, the data model generator 118 generates a significance matrix of the one or more second variables.
In one embodiment, the data model generator 118 generates the significance matrix based on the determined significance factor (k). The significance matrix may be a matrix of the significance factor (k) mapped against the plurality of discrete intervals (alternatively referred to as significance intervals) of one or more second variables. Based on the significance matrix, the data model generator 118 determines the data model by mapping the corresponding forecasted statistical summary value of the one or more second variables. Upon generating the data model, the future forecasted value of data is determined.
In one embodiment, the FFVD module 220 determines the future forecasted value of data based on the historically determined forecasted data value stored in the HD database 106 and the data model thus generated. The data updating module 222 dynamically updates the future forecasted value of data, the generated data model and actual value corresponding to the forecast in the HD database 106 for further forecasting of data.
Thus, the present disclosure dynamically generates a data model for forecasting data related to the event that reduces the errors that arise during forecasting by considering all factors or variables and improve the efficiency of the data forecasting.
Figure 3 illustrates a flowchart of an exemplary method of dynamically generating a data model for forecasting data related to an event in accordance with some embodiments of the present disclosure;
As illustrated in Figure 3, the method 300 comprises one or more blocks implemented by the processor 110 for dynamically generating a data model for forecasting data related to the event. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.
The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof.
At block 302, receive historic dataset. In one embodiment, the data modelling system 102 receives the plurality of historic dataset 208 from the HD database 106. The plurality of historic dataset 208 comprises one or more data corresponding to the one or more first variables and data associated with the one or more second variables.
Let us consider an illustrating scenario, where the data modelling system 102 receives historic dataset of for example, error data related to forecasting of power consumption data obtained at particular time at a particular location. The data modelling system 102 aims at error modeling to reduce the errors thus obtained during the forecasting of power consumption data so as to monitor the power consumption at different locations at different time periods and reduce the unused energy and thereby improve the efficiency of power grid operation. Actual data related to energy consumption captured during past time at different locations is stored in the HD database 106. Further, corresponding forecasted data and error data obtained due to the difference between the forecasted and actual data are stored in the HD database 106. Thus, the plurality of historic dataset comprises error data as data of the one or more first variables (y) and temporal, spatial/geographical data as data of the one or more second variables (a b). In one example, let us consider that the HD database 106 may comprise data from an electric utility i.e., 24 hours error data of 1st to 3rd April 2014 and the data modelling system 102 forecast the load of 8th April 2014 at 2AM at location 1.
At block 304, determine reference statistical summary value. In one embodiment, the SSVA 114 determines the reference statistical summary value 210 of the plurality of historic dataset 208. In one example, the SSVA 114 determines the reference mean value (µr) of the plurality of historic dataset based on data related to the one or more first variables. In one aspect, the probability distribution of the error data is graphically determined along the time. As illustrated, the reference mean value µr of the historic error data is determined i.e., µr = 389 MW.
At block 306, classify the historic dataset into dataset groups. In one embodiment, the dataset classifier 116 dynamically classifies the plurality of historic dataset 208 based on data related to the one or more second variables. The dataset classifier 116 selects the one or more second variables and determines boundary values of the one or more selected second variables. Based on the boundary values, the dataset classifier 116 repeatedly determines a plurality of discrete interval values and a step-size corresponding to the plurality of discrete interval values. As illustrated, the dataset classifier 116 generates the plurality of dataset groups i.e., the error data at 2 AM at location 1 for the three days.
At block 308, determine specific statistical summary value of each dataset group. For each of the plurality of classified dataset groups, the SSVA 114 determines the specific statistical summary value 212 using known techniques. In one example, the SSVA 114 determines the specific mean value (µ1) of each of the plurality of classified dataset groups based on data related to the one or more second variables. As per the illustration, the SSVA 114 determines the specific mean value µ1 based on the error data at 2AM at location 1 for the three days i.e., µ1 = 292 MW.
At block 310, generate a data model. In one embodiment, the data model generator 118 compares the reference statistical summary value 210 and the specific statistical summary value 212 and determines a statistical significance factor (k) based on the comparison. The significance factor (k) indicates as to whether there exists a substantial difference or not between the reference statistical summary value 210 and the specific statistical summary value 212. The value of k may be for example, between 0 and 1. If the data model generator 118 determines that there exists a substantial difference, then the significance factor (k) is set equals to 1 and the specific statistical summary value 212 is determined as the forecasted statistical summary value. On the other hand, if the data model generator 118 determines that there exists no substantial difference, and then the significance factor (k) is set equals to 0 and the reference statistical summary value 212 is determined as the forecasted statistical summary value. As illustrated, the data model generator 118 determines a significant difference between µr and µ1, therefore, the value of significance factor k is set to 1. Based on the forecasted statistical summary value thus determined, the data model generator 118 generates a significance matrix of the one or more second variables.
In one embodiment, the data model generator 118 generates the significance matrix based on the determined significance factor (k). The significance matrix may be a matrix of the significance factor (k) mapped against the plurality of discrete intervals (alternatively referred to as significance intervals) of one or more second variables. For example, the second variable ‘a’ may be the temporal variable for example, time of the day say 1AM or 2 AM and ‘b’ may be spatial variable, for example geographic location 1 and geographic location 2.

a
1 AM 2 AM
b Location 1 0 1
Location 2 0 1

At the location 1, it may be noted that the temporal variable at 2AM has high significance interval in the data model thus generated.
Based on the significance matrix, the data model generator 118 determines the data model by mapping the corresponding the forecasted statistical summary value of the one or more second variables as illustrated below in equation (1).
y1 = µr * (1-k) + µ1 * k …………………………………………….. (1)
Upon generating the data model, the future forecasted value of data is determined. In one embodiment, the FFVD module 220 determines the future forecasted value of data based on the historically determined forecasted data value stored in the HD database 106 and the data model of error thus generated.
An illustration of comparison of results of conventional and proposed data model is shown below:
Conventional data model Exemplary data model
Error model y= µr y1 = µr * (1-k) + µ1 * k
Error model output 389 MW 292 MW (since k=1)
New Forecast value 13306 + 389 = 13695 MW 13306 + 292 = 13598 MW
Actual load 13595 13595
New Error 100 MW 3 MW

The data updating module 22 then dynamically updates the future forecasted value of data, the generated data model in the HD database 106 for further forecasting of data.
Figure 4 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
Variations of computer system 401 may be used for implementing all the computing systems that may be utilized to implement the features of the present disclosure. Computer system 401 may comprise a central processing unit (“CPU” or “processor”) 402. Processor 402 may comprise at least one data processor for executing program components for executing user- or system-generated requests. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor 402 may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor 402 may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.

Processor 402 may be disposed in communication with one or more input/output (I/O) devices via I/O interface 403. The I/O interface 403 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

Using the I/O interface 403, the computer system 401 may communicate with one or more I/O devices. For example, the input device 404 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, visors, etc. Output device 405 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, or the like), audio speaker, etc. In some embodiments, a transceiver 406 may be disposed in connection with the processor 402. The transceiver may facilitate various types of wireless transmission or reception. For example, the transceiver may include an antenna operatively connected to a transceiver chip (e.g., Texas Instruments WiLink WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some embodiments, the processor 402 may be disposed in communication with a communication network 408 via a network interface 407. The network interface 407 may communicate with the communication network 408. The network interface 407 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/40/400 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 408 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 407 and the communication network 408, the computer system 401 may communicate with devices 409, 410, and 411. These devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system 401 may itself embody one or more of these devices.

In some embodiments, the processor 402 may be disposed in communication with one or more memory devices (e.g., RAM 413, ROM 414, etc.) via a storage interface 412. The storage interface may connect to memory devices including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), integrated drive electronics (IDE), IEEE-1394, universal serial bus (USB), fiber channel, small computer systems interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, redundant array of independent discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory 415 may store a collection of program or database components, including, without limitation, an operating system 416, user interface application 417, web browser 418, mail server 419, mail client 420, user/application data 421 (e.g., any data variables or data records discussed in this disclosure), etc. The operating system 416 may facilitate resource management and operation of the computer system 401. Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like. User interface 417 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to the computer system 401, such as cursors, icons, check boxes, menus, scrollers, windows, widgets, etc. Graphical user interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, web interface libraries (e.g., ActiveX, Java, Javascript, AJAX, HTML, Adobe Flash, etc.), or the like.

In some embodiments, the computer system 401 may implement a web browser 418 stored program component. The web browser may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), etc. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, application programming interfaces (APIs), etc. In some embodiments, the computer system 301 may implement a mail server 419 stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as internet message access protocol (IMAP), messaging application programming interface (MAPI), Microsoft Exchange, post office protocol (POP), simple mail transfer protocol (SMTP), or the like. In some embodiments, the computer system 401 may implement a mail client 420 stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc.

In some embodiments, computer system 401 may store user/application data 421, such as the data, variables, records, etc. as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object-oriented databases (e.g., using ObjectStore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of the any computer or database component may be combined, consolidated, or distributed in any working combination.

As described above, the modules 206, amongst other things, include routines, programs, objects, components, and data structures, which perform particular tasks or implement particular abstract data types. The modules 206 may also be implemented as, signal processor(s), state machine(s), logic circuitries, and/or any other device or component that manipulate signals based on operational instructions. Further, the modules 206 can be implemented by one or more hardware components, by computer-readable instructions executed by a processing unit, or by a combination thereof.

The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., are non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.