DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEMS AND METHODS FOR FORECASTING KEY PERFORMANCE INDICATORS FOR ENTITIES

Applicant:
Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th Floor,
Nariman Point, Mumbai 400021,
Maharashtra, India

The following specification particularly describes the invention and the manner in which it is to be performed.
CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims priority from Indian provisional patent application no. 202221041902, filed on July 21, 2022. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD
[002] The disclosure herein generally relates to forecasting techniques, and, more particularly, to systems and methods for forecasting key performance indicators for entities.

BACKGROUND
[003] Forecasting for Key Performance Indicators (KPIs) is a common requirement across organizations and enterprises. Various forecasting methods have been proposed to arrive at reasonably accurate forecasts. These existing methods used statistical analysis, machine meaning, artificial intelligence, etc. Some of these used distribution pattern over and above other methods. Despite these efforts, the accuracy is not up to the mark and needs some improvement.

SUMMARY
[004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems.
[005] For example, in one aspect, there is provided a processor implemented method for forecasting key performance indicators for entities. The method comprises obtaining, via one or more hardware processors, an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period, wherein the one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to the entity; estimating, via the one or more hardware processors, a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals comprised in the first time period; estimating, via the one or more hardware processors, the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals comprised in a second time period; estimating, via the one or more hardware processors, the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval of a third time period; performing a comparison of (i) a plurality of time slots of a current time interval of the first time period for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals comprised in the second time period and (b) the specific time interval of the third time period; identifying one or more changing parameters based on the comparison, wherein the one or more changing parameters comprises at least one associated status; identifying a set of strategies based on the associated status of the one or more changing parameters; and applying the set of strategies across the plurality of time slots of the current time interval to obtain a forecasted output for the one or more KPIs pertaining to the entity.
[006] In an embodiment, the one or more changing parameters comprise at least one of number of time slots in a specific time interval, and number of pre-defined time slots, a first pre-defined time slot, a difference in the pre-defined time slots, the last pre-defined time slot in a previous time period with reference to the first time period, and a second pre-defined time slot.
[007] In an embodiment, the method comprises analysing the forecasted output with reference to one or more previously forecasted outputs to obtain an analysed forecasted output.
[008] In an embodiment, the method comprises iteratively performing generating a subsequent set of strategies based on the analysed forecasted output; and applying the subsequent set of strategies on the first time period to obtain an updated forecasted output, until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage.
[009] In another aspect, there is provided a processor implemented system for forecasting key performance indicators for entities. The system comprises: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to obtain an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period, wherein the one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to the entity; estimate a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals comprised in the first time period; estimate the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals comprised in a second time period; estimate the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval of a third time period; perform a comparison of (i) a plurality of time slots of a current time interval of the first time period for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals comprised in the second time period and (b) the specific time interval of the third time period; identify one or more changing parameters based on the comparison, wherein the one or more changing parameters comprises at least one associated status; identify a set of strategies based on the associated status of the one or more changing parameters; and apply the set of strategies across the plurality of time slots of the current time interval to obtain a forecasted output for the one or more KPIs pertaining to the entity.
[010] In an embodiment, the one or more changing parameters comprises at least one of number of time slots in a specific time interval, and number of pre-defined time slots, a first pre-defined time slot, a difference in the pre-defined time slots, the last pre-defined time slot in a previous time period with reference to the first time period, and a second pre-defined time slot.
[011] In an embodiment, the one or more hardware processors are further configured by the instructions to analyze the forecasted output with reference to one or more previously forecasted outputs to obtain an analysed forecasted output.
[012] In an embodiment, the one or more hardware processors are further configured by the instructions to iteratively perform: generating a subsequent set of strategies based on the analysed forecasted output; and applying the subsequent set of strategies on the first time period to obtain an updated forecasted output, until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage.
[013] In yet another aspect, there are provided one or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause forecasting key performance indicators for entities by obtaining an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period, wherein the one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to an entity; estimating a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals comprised in the first time period; estimating the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals comprised in a second time period; estimating the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval of a third time period; performing a comparison of (i) a plurality of time slots of a current time interval of the first time period for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals comprised in the second time period and (b) the specific time interval of the third time period; identifying one or more changing parameters based on the comparison, wherein the one or more changing parameters comprises at least one associated status; identifying a set of strategies based on the associated status of the one or more changing parameters; and applying the set of strategies across the plurality of time slots of the current time interval to obtain a forecasted output for the one or more KPIs pertaining to the entity.
[014] In an embodiment, the one or more changing parameters comprises at least one of number of time slots in a specific time interval, and number of pre-defined time slots, a first pre-defined time slot, a difference in the pre-defined time slots, the last pre-defined time slot in a previous time period with reference to the first time period, and a second pre-defined time slot.
[015] In an embodiment, the one or more instructions which when executed by the one or more hardware processors further cause analysing the forecasted output with reference to one or more previously forecasted outputs to obtain an analysed forecasted output.
[016] In an embodiment, the one or more instructions which when executed by the one or more hardware processors further cause iteratively performing: generating a subsequent set of strategies based on the analysed forecasted output; and applying the subsequent set of strategies on the first time period to obtain an updated forecasted output, until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage.
[017] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[018] 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:
[019] FIG. 1 depicts an exemplary system for forecasting one or more Key Performance Indicators (KPIs) for an entity, in accordance with an embodiment of the present disclosure FIG. 2 is a functional block diagram according to some embodiments of the present disclosure.
[020] FIG. 2 depicts an exemplary flow chart illustrating a method for forecasting one or more Key Performance Indicators (KPIs) for the entity, using the system of FIG. 1, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[021] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[022] Forecasting for Key Performance Indicators (KPIs) is a common requirement across organizations and enterprises. Various forecasting methods have been proposed to arrive at reasonably accurate forecasts. These existing methods used statistical analysis, machine meaning, artificial intelligence, etc. Some of these used distribution pattern over and above other methods. Despite these efforts, the accuracy is not up to the mark and need some improvement. As explained above, accuracy of forecast is very important for prediction of various KPIs. The expression ‘prediction’ and ‘forecasting’ may be interchangeably used herein. However, many automated as well as manual methods do not provide the desired accuracy. Some of the existing methods that forecast still face certain challenge such as: statistical methods are preliminary, deep learning method may or may not provide accuracy as compared to machine learning (ML) methods, based on the unknown distribution of data. Further, these conventional methods work better when granularity is low (e.g., quarterly, monthly, etc.). For highly granular data (e.g., daily, hourly, etc.) the forecasting accuracy reduces significantly. Such challenges may result into less than expected forecast accuracy, thereby leading to reduced confidence in various stakeholders in an entity (e.g., organization). Conventionally available AutoML tools provide the configuration screens for known methods of accuracy improvement, however these are applicable to limited and specific use cases.
[023] Further, weekends are different in different months, and number of days for every month are not same. Hence, leap year also creates challenges. Therefore, mapping previous period with forecasted month is a unique combination every time. All in all, adjusting weekends to improve forecast accuracy, at scale, is a huge task. If this complexity is true for just one KPI, one can only imagine the case of integrated planning scenario in the organization where multiple KPIs are needed to be planned simultaneously, with their own hierarchies. Thus, weekend Adjustment becomes a humongous task. Current methods and technologies do not infuse (business) intelligence in each, and every use case. For example, how does one find out if 1) behavior of same month previous year or 2) behavior of same month previous quarter is mimicking a distribution pattern of to be forecasted for a given month? Depending on the geography, pattern, the behavior of time series shall change. Hence granular method to analyze the behavior pattern of previous period shall be useful. Embodiments of the present disclosure provide systems and methods for forecasting KPIs for dataset of interest that pertain to one or more entities (e.g., organization, enterprise, and the like). More specifically, the system and method estimate monthly forecasting, wherein data from various dataset are read along with KPIs. The read data is preprocessed to ensure that it can be fitted into forecasting system/method of the present disclosure, wherein various strategies are applied to derive forecasted output. In other words, data related to KPIs is processed for monthly forecasting using one or more Artificial Intelligence (AI) or Machine Learning (ML) models as known in the art. The daily distribution of the data/KPIs is observed and taken from previous period for all months in a year. The previous period includes previous year same month and previous quarter same month, in one example embodiment. Further, changing parameters are identified and accordingly distribution is adjusted. The distribution adjustment includes predicting the distribution based on (i) previous year same month and (ii) previous quarter same month. Depending on the distribution and adjustment analysis, appropriate previous period is selected. The system and method implement one or more AI/ML techniques for predicting a forecasted output that is indicative of KPIs of a monthly forecast (e.g., say KPIs for current month or subsequent months, KPIs for current day or subsequence days, KPIs for current hour or subsequent hours, KPIs for current time interval/slot (e.g., say 15 minutes slot or say between 9.15 AM to 9.30 AM), and the like.
[024] Referring now to the drawings, and more particularly to FIGS. 1 through 2, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[025] FIG. 1 depicts an exemplary system 100 for forecasting one or more Key Performance Indicators (KPIs) for an entity, in accordance with an embodiment of the present disclosure. In an embodiment, the system 100 includes one or more hardware processors 104, communication interface device(s) or input/output (I/O) interface(s) 106 (also referred as interface(s)), and one or more data storage devices or memory 102 operatively coupled to the one or more hardware processors 104. The one or more processors 104 may be one or more software processing components and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is/are configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the system 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices (e.g., smartphones, tablet phones, mobile communication devices, and the like), workstations, mainframe computers, servers, a network cloud, and the like.
[026] The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server.
[027] The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic-random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, a database 108 is comprised in the memory 102, wherein the database 108 comprises various Key Performance Indicators (KPIs) comprised in various dataset that are associated with various entities (e.g., organization, companies, financial institution, business entities, and the like). The database 108 further comprises historical data pertaining to the KPIs, dataset(s), entities, and the like. The memory 102 comprises one or more Artificial Intelligence (AI) or Machine Learning (ML) models as known in the art. The system 100 or the one or more hardware processors 104 invoke the one or more Artificial Intelligence (AI) or Machine Learning (ML) models to perform the method and/or the steps of the method described in FIG. 2. The memory 102 further comprises (or may further comprise) information pertaining to input(s)/output(s) of each step performed by the systems and methods of the present disclosure. In other words, input(s) fed at each step and output(s) generated at each step are comprised in the memory 102 and can be utilized in further processing and analysis.
[028] FIG. 2 depicts an exemplary flow chart illustrating a method for forecasting one or more Key Performance Indicators (KPIs) for the entity, using the system 100 of FIG. 1, in accordance with an embodiment of the present disclosure. In an embodiment, the system(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to components of the system 100 of FIG. 1, and the flow diagram as depicted in FIG. 2.
[029] At step 202 of the method of the present disclosure, the one or more hardware processors 104 obtain an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period. The one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to an entity (e.g., say an enterprise, an organization, and the like). In the present disclosure, the system and method have considered invoice details or invoice data as the one or more KPIs. It is to be understood by a person having ordinary skill in the art or person skilled in the art that such examples of considering invoice data as KPIs shall not be construed as limiting the scope of the present disclosure. Other KPIs that can be considered as input to the system 100 for obtaining updated forecasted output may include but are not limited to, potential/candidate likelihood of sales prediction, future candidate customer engagements, and the like. The first time period may refer to a specific year (or (e.g., say current year), in an example embodiment of the present disclosure. Below Table 1 illustrates invoice details for the specific year:
Table 1
Month Invoice
January 239,867,923
February 217,399,534
March 303,271,002
April 226,988,613
May 246,118,848
June 300,889,675
July 220,648,933
August 237,568,115
September 307,852,026
October 248,029,128
November 244,755,847
December 332,810,947

[030] At step 204 of the method of the present disclosure, the one or more hardware processors 104 estimate a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals (all months) comprised in the first time period (e.g., say current year).
[031] At step 206 of the method of the present disclosure, the one or more hardware processors 104 estimate the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals (same all months) comprised in a second time period (e.g., previous year). In the present disclosure, the Below Table 2 illustrates the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals in the second time period.
Table 2
Jan-Yearly-2019 Predicted Distribution
0
1
1
1
0
0
1
1
2
2
2
0
0
1
1
2
1
3
1
0
3
2
3
5
4
1
1
2
16
22
23

[032] Typically, the distribution is measured in terms of hundreds. However, in the above Table 2, sum of the KPIs distribution may not amount to 100. Reason being, there could be distribution in decimal points which are not considered while the above Table 2 is illustrated herein. Hence, the total may not add up to 100. It is to be understood to person having ordinary skill in the art or person skilled in the art that decimal points are (or may be) considered for computation/analysis purpose that may be used for forecasting/prediction of KPIs for subsequent time period (or future time interval).
[033] At step 208 of the method of the present disclosure, the one or more hardware processors 104 estimate the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval (e.g., a specific month) of a third time period (e.g., previous quarter same month).
[034] Below Table 3 illustrates the distribution of the one or more Key Performance Indicators (KPIs) in the specific time interval (e.g., January (or Jan) as specific month) of the third time period.
Table 3
Jan-Quaterly-2019 Predicted Distribution
0
0
0
1
0
0
1
1
1
2
3
0
0
2
2
2
2
1
0
0
0
3
3
3
4
2
2
9
18
20
19

[035] At step 210 of the method of the present disclosure, the one or more hardware processors 104 perform a comparison of (i) a plurality of time slots (e.g., days) of a current time interval of the first time period (e.g., forecasted period) for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals (same all months) comprised in the second time period and (b) the specific time interval (e.g., a specific month) of the third time period. In this Relevant comparison of days of months between the two periods (i.e., (Previous Month same month or Previous Month same quarter) and the Forecasted Period) is done. This comparison helps to find out the required parameters to identify one or more patterns for adjustments (e.g., weekend adjustment).
[036] At step 212 of the method of the present disclosure, the one or more hardware processors 104 identify one or more changing parameters based on the comparison. The one or more changing parameters comprises at least one associated status. Such associated status may include a Boolean status indicative or 0 or 1. For the given example, the one or more changing parameters comprise at least one of number of time slots in a specific time interval (e.g., month), number of pre-defined time slots (e.g., working days), a first pre-defined time slot (e.g., last working day), a difference in the pre-defined time slots (e.g., a working week/weekend), the last pre-defined time slot in a previous time period (e.g., previous year/month/day last working day/hour) with reference to the first time period, and a second pre-defined time slot (e.g., last working week/weekend). The Boolean status indicative of 1 refers to presence of the one or more changing parameters mentioned above. The Boolean status indicative of 0 refers to absence of the one or more changing parameters mentioned above.
[037] At step 214 of the method of the present disclosure, the one or more hardware processors 104 identify a set of strategies based on the associated status of the one or more changing parameters.
[038] At step 216 of the method of the present disclosure, the one or more hardware processors 104 apply the set of strategies across the plurality of time slots (days) of the current time interval to obtain a forecasted output for the one or more KPIs. The steps 214 and 216 are better understood by way of following description:
[039] For different permutations and combinations, one or more strategies are identified for adjusting various parameters (e.g., in the given example, adjust weekends). In other words, based on the above changing parameters and their associated status, various unique combination of patterns is created which defines the strategy for the weekend adjustment. Combination of patterns may include, but are not limited to, monitoring past period distributions that are likely to be closer to daily forecast for month, and selecting appropriate (or relevant) past periods that provided consistently better accuracy, and the like.
[040] For each strategy set of actions is find out on the percentage distribution of the past period (pervious quarter same month / pervious year same month) based on which weekend adjustment is done. For each strategy, one or more actions may be performed. The system 100 may generate function(s) for each action. In an embodiment, the functions may be also obtained from one or more users via appropriate user interfaces implemented by the system 100. Such functions may be generated/created by the system 100 itself (without having to obtain functions from the users) for each of the action performed which performs the weekend adjustment. These actions are adjusting the weekend distribution and move the distribution based on certain rules (e.g., pre-configured rules). They can be repetitive for a particular period for forecasting. More specifically, a pre-configured rule may include such as ‘weekend is adjusted by moving distribution numbers accordingly to target month’ (e.g., moving the daily distribution value of the previous period month to the target period month).
[041] Below Table 4 depicts weekend adjustment serving as one of the strategies applied by the system 100 (or the one or more hardware processors 104) across the plurality of time slots (days) of the current time interval to obtain the forecasted output for the one or more KPIs. The current time interval is for Jan-Mar 2018 and 2019.
Table 4
Days Predicted_01_2019 Actual 01-2018 Predicted_02_2019 Actual 02-2018 Predicted_03_2019 Actual 03-2018
1 0 0 1 1 1 1
2 1 1 0 1 0 1
3 1 1 0 0 0 0
4 1 1 1 0 1 0
5 0 1 1 1 2 1
6 0 0 1 1 1 2
7 1 0 1 1 1 1
8 1 1 2 2 1 1
9 2 2 2 4 0 1
10 2 2 0 2 0 0
11 2 2 4 0 1 0
12 0 1 1 1 1 1
13 0 0 3 3 1 1
14 1 0 0 0 1 1
15 1 1 2 1 2 1
16 2 2 0 1 0 2
17 1 1 0 0 0 0
18 3 3 1 0 2 0
19 1 3 1 1 1 2
20 0 1 2 2 3 1
21 3 0 3 3 4 3
22 2 2 4 4 6 4
23 3 3 1 5 1 6
24 5 5 1 1 1 1
25 4 4 5 1 10 1
26 1 2 19 19 13 10
27 1 1 21 21 15 13
28 2 1 23 23 17 15
29 16 16 12 17
30 22 22 1 12
31 23 23 1 1

[042] The strategy applied by the system 100 or the one or more hardware processors 104 for Actual_1_2018 is move Friday towards right. Similarly, the strategy taken by the system 100 or the one or more hardware processors 104 for Actual_2_2018 is move Friday towards right. The one or more strategies taken by the system 100 or the one or more hardware processors 104 for Actual_3_2018 are firstly moving diagonally left/right by ‘x’ place (say 1 place, 2 places, or by ‘m’ places) and copying first value into last and/or penultimate places. Other strategies may further include, moving Thursday, Friday Towards Right, and/or moving Monday towards left. The adjustments are shown in bold numbers in the above Table 4. The above strategies may also serve as pre-configured rules as mentioned above.
[043] It is to be understood by a person having ordinary skill in the art or person skilled in the art that though the strategies applied by the system 100 or the one or more hardware processors 104 are shown for January to March months, such examples shall not be construed as limiting the scope of the present disclosure. In other words, one or more similar strategies can be applied by the system 100 or the one or more hardware processors 104 for remaining months and combinations such as April till June, July till September, and/or October till December. Depending on the data observed by the system 100, other strategies may include, but are not limited to, (a) moving Friday towards right and inserting first value as it is, (b) moving diagonally left by 1 place, and inserting the first value into a last value, (c) moving Friday towards right and inserting a first value to a second place, and the like. It is to be further understood by a person having ordinary skill in the art or person skilled in the art thought the current strategies are applied for quarterly basis, such examples shall not be construed as limiting the scope of the present disclosure. In other words, the month to days or days to hours or hours to minutes adjustments can also be made by the system 100 or the one or more hardware processors 104.
[044] The forecasted output is analyzed with reference to one or more previously forecasted outputs to obtain an analysed forecasted output. In such scenarios, the forecasted output may be related to July month 2022 and the one or more previously forecasted outputs may be related to April month 2022 (e.g., same year previous quarter) versus July 2021 (same month previous year). Further, the one or more hardware processors 100 generate a subsequent set of strategies based on the analysed forecasted output and apply the subsequent set of strategies on the first time period to obtain an updated forecasted output. The one or more hardware processors 100 perform the step of generating the subsequent set of strategies and applying these on the first time period until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage. For instance, say the pre-defined forecast threshold is x% (e.g., 95%), and the pre-defined percentage is y% (5%). Hence the above steps are iteratively performed by the system 100 until the updated forecasted output is less than or equal to the pre-defined percentage is y%. It is to be understood by a person having ordinary skill in the art that in reality arriving at such difference may not be an immediate result. Hence, the difference or reduction in the error between the forecasted output and the pre-defined forecast threshold may be observed in subsequent time periods (e.g., in coming days, or months, or years) which depicts the accuracy of the predicted or forecasted output pertaining to the KPIs.
As explained above, weekends are different in different months. Number of days for every month are not same, and leap year creates challenges. Mapping previous period with forecasted month is a unique combination every time. All in all, adjusting weekends to improve forecast accuracy, at scale, is a huge task. If this complexity is true for just one KPI, one can only imagine the case of integrated planning scenario in the organization where multiple KPIs are needed to be planned simultaneously, with their own hierarchies. Adjustment of data/KPIs becomes a humongous task. Existing systems and method do not infuse business intelligence in each and every use case. How does one find out if 1) behavior of same month previous year or 2) behavior of same month previous quarter is mimicking the distribution pattern of forecasted month? Further, depending on geography, pattern, and the like the behavior of time series shall change. Hence, granular method to analyze the behavior pattern of previous period shall be useful. While existing systems and methods provide common solutions such as use of preprocessing techniques, Neural Network and forecasting, these solutions have the following drawbacks such as forecasted output is prone to error due to change in distribution pattern, it does not provide prominent accuracy at day level. Embodiments of the present disclosure provide systems and methods that forecast KPIs for dataset of interest that pertain to one or more entities (e.g., organization, enterprise, and the like). More specifically, the system and method estimate monthly forecasting, wherein data from various dataset are read along with KPIs. The read data is preprocessed to ensure that it can be fitted into forecasting system/method of the present disclosure, wherein various strategies are applied to derive forecasted output. In other words, data related to KPIs is processed for monthly forecasting using one or more Artificial Intelligence (AI) or Machine Learning (ML) models as known in the art. The daily distribution of the data/KPIs is observed and taken from previous period for all months in a year. The previous period includes previous year same month and previous quarter same month, in one example embodiment. Further, changing parameters are identified and accordingly distribution is adjusted. The distribution adjustment includes predicting the distribution based on (i) previous year same month and (ii) previous quarter same month. Depending on the distribution and adjustment analysis, appropriate previous period is selected. The system and method implement one or more AI/ML techniques for predicting a forecasted output that is indicative of KPIs of a monthly forecast (e.g., say KPIs for current month or subsequent months, KPIs for current day or subsequence days, KPIs for current hour or subsequent hours, KPIs for current time interval/slot (e.g., say 15 minutes slot or say between 9.15 AM to 9.30 AM), and the like. Such forecasted output of KPIs can be used in various industries such as Energy, Transportation, Finance, Manufacturing, Media and Entertainment, etc. In other words, use cases may include but are not limited to, Billing, Energy Consumption, Production, Sales of tickets (e.g., Train tickets, flight tickets, etc.) can be applied where weekend value is less. In addition to this further use cases can include forecasting sales of movie tickets, sales of hotel rooms, etc. can be applied when weekend value is prominent. The method of the present disclosure can also be applied on non-forecasting use case (agnostic to Industries) such as copying and mapping a timetable from previous period to target period based on baseline that is created that can be adjusted further.
[045] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[046] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[047] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[048] 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 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.
[049] 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., be 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.
[050] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.

,CLAIMS:
1. A processor implemented method, comprising:
obtaining, via one or more hardware processors, an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period (202), wherein the one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to an entity;
estimating, via the one or more hardware processors, a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals comprised in the first time period (204);
estimating, via the one or more hardware processors, the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals comprised in a second time period (206);
estimating, via the one or more hardware processors, the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval of a third time period (208);
performing, via the one or more hardware processors, a comparison of (i) a plurality of time slots of a current time interval of the first time period for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals comprised in the second time period and (b) the specific time interval of the third time period (210);
identifying, via the one or more hardware processors, one or more changing parameters based on the comparison, wherein the one or more changing parameters comprises at least one associated status (212);
identifying, via the one or more hardware processors, a set of strategies based on the associated status of the one or more changing parameters (214); and
applying, via the one or more hardware processors, the set of strategies across the plurality of time slots of the current time interval to obtain a forecasted output for the one or more KPIs pertaining to the entity (216).

2. The processor implemented method as claimed in claim 1, wherein the one or more changing parameters comprise at least one of number of time slots in a specific time interval, and number of pre-defined time slots, a first pre-defined time slot, a difference in the pre-defined time slots, the last pre-defined time slot in a previous time period with reference to the first time period, and a second pre-defined time slot.

3. The processor implemented method as claimed in claim 1, comprising analysing the forecasted output with reference to one or more previously forecasted outputs to obtain an analysed forecasted output.

4. The processor implemented method as claimed in claim 1, comprising iteratively performing:
generating a subsequent set of strategies based on the analysed forecasted output; and
applying the subsequent set of strategies on the first time period to obtain an updated forecasted output, until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage.

5. A system (100), comprising:
a memory (102) storing instructions;
one or more communication interfaces (106); and
one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), wherein the one or more hardware processors (104) are configured by the instructions to:
obtain an input comprising one or more Key Performance Indicators (KPIs) specific to a first time period, wherein the one or more Key Performance Indicators (KPIs) are associated with a dataset of interest pertaining to an entity;
estimate a distribution of the one or more Key Performance Indicators (KPIs) in a plurality of time intervals comprised in the first time period;
estimate the distribution of the one or more Key Performance Indicators (KPIs) in the plurality of time intervals comprised in a second time period;
estimate the distribution of the one or more Key Performance Indicators (KPIs) in a specific time interval of a third time period;
perform a comparison of (i) a plurality of time slots of a current time interval of the first time period for which the one or more Key Performance Indicators (KPIs) are to be forecasted and (ii) at least one of (a) the plurality of time intervals comprised in the second time period and (b) the specific time interval of the third time period;
identify one or more changing parameters based on the comparison, wherein the one or more changing parameters comprises at least one associated status;
identify a set of strategies based on the associated status of the one or more changing parameters; and
apply the set of strategies across the plurality of time slots of the current time interval to obtain a forecasted output for the one or more KPIs pertaining to the entity.

6. The system as claimed in claim 5, wherein the one or more changing parameters comprise at least one of number of time slots in a specific time interval, and number of pre-defined time slots, a first pre-defined time slot, a difference in the pre-defined time slots, the last pre-defined time slot in a previous time period with reference to the first time period, and a second pre-defined time slot.

7. The system as claimed in claim 5, wherein the one or more hardware processors are further configured by the instructions to analyze the forecasted output with reference to one or more previously forecasted outputs to obtain an analysed forecasted output.

8. The system as claimed in claim 5, wherein the one or more hardware processors are further configured by the instructions to iteratively perform:
generating a subsequent set of strategies based on the analysed forecasted output; and
applying the subsequent set of strategies on the first time period to obtain an updated forecasted output, until a difference between the updated forecasted output and a pre-defined forecast threshold is reduced by a pre-defined percentage.