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 computer readable medium for a method for forecasting Office Actions for a patent portfolio The method includes performing predefined statistical analysis on a set of patents, wherein the set of patents and the patent portfolio have similar patent distribution; identifying one or more Office Action parameters associated with the set of patents and the patent portfolio based on the predefined statistical analysis performed on the set of patents; and estimating the number of Office Actions issued for the patent portfolio based on the predefined statistical analysis performed on the set of patents and the identified Office Action parameters identified.