Abstract: Systems and methods for forecasting a time series data are disclosed. The methods include receiving a historical time-series data including a series data and a non-stationary series data. The historical time-series data is processed to obtain a unified time series data. On the unified time series data, a data distribution is plotted and the data distribution is validated based upon a rate function associated with a Large Deviation Theory (LDT). The unified time series data is split validated into vectors based on autocorrelation function (ACF). The unified time series data is further validated. A mixture of Gaussian distribution models is applied and weights are assigned to each of the Gaussian distribution model. By controlling the weights based upon various what-if scenarios, a resultant Gaussian time series data is generated. The resultant Gaussian time series data indicates forecasted time series data of the historical time series data.

Abstract: Systems and methods for forecasting a time series data are disclosed. The methods include receiving a historical time-series data including a series data and a non-stationary series data. The historical time-series data is processed to obtain a unified time series data. On the unified time series data, a data distribution is plotted and the data distribution is validated based upon a rate function associated with a Large Deviation Theory (LDT). The unified time series data is split validated into vectors based on autocorrelation function (ACF). The unified time series data is further validated. A mixture of Gaussian distribution models is applied and weights are assigned to each of the Gaussian distribution model. By controlling the weights based upon various what-if scenarios, a resultant Gaussian time series data is generated. The resultant Gaussian time series data indicates forecasted time series data of the historical time series data.

Abstract: A method and system for determining customer lifetime value (CLV) for a business is described. The method may include receiving, from a user, inputs associated with a plurality of parameters. The plurality of parameters correspond to customer transactions. The method may further include determining, based on the received inputs, a CLV base model applicable for the business. The CLV base model is determined from amongst a plurality of pre-defined CLV base models. Further, the method may include identifying at least one market scenario from amongst a plurality of pre-configured market scenarios for the business. Each of the plurality of pre-configured market scenarios are based on a combination of the plurality of parameters. The method may also include computing a consolidated CLV for the business based on the determined CLV base model and the at least one identified CLV scenario model.

Abstract: Systems and methods described herein relate to brand positioning and promotion impact evaluation for stationary sales brands. According to one embodiment of the present subject matter, an impact output indicative of the competitive brand positioning of each of a plurality of stationary sales brands is generated based on identification of a hidden co-integration relationship that may exist between pairs of brands from amongst the plurality of stationary sales brands. Further, according to another embodiment of the present subject matter, impact of promotional variables on sales of a stationary sales brand is evaluated based on identifying hidden co-integration relationship between the promotional variables and the sales of the stationary sales brand.