1. A method for forecasting a time series data, the method comprising: receiving, by a processor, a historical time series data comprising a stationary series data and a non-stationary series data, wherein the non-stationary series data indicate change in a mean and a variance of consecutive values of the historical time series data; processing, by the processor, the stationary series data and the non-stationary series data to form a unified time series data, wherein the processing comprises converting the non-stationary series data to into a differenced stationary series data, wherein the non-stationary series data is converted by differencing the consecutive values of the non-stationary series data, and combining the differenced stationary series data and the stationary series data in order to form the unified time series data; plotting, by the processor, a data distribution for the unified time series data; validating, by the processor, the data distribution of the unified time series data based upon a rate function, wherein the rate function is associated with a Large Deviation Theory (LDT); splitting, by the processor, the unified time series data into vectors based on an autocorrelation function (ACF), wherein each vector comprises a set of values; representing, by the processor, data distribution of each vector into a mixture of Gaussian distribution models; assigning, by the processor, weights to each Gaussian distribution model using a Maximum Likelihood Estimation or Expectation Maximization (MLE/EM) algorithm, wherein the weights are assigned based on mean values of the Gaussian mixture models; and generating, by the processor, a resultant Gaussian time series data, wherein the resultant Gaussian time series data is modified/varied by controlling the weights, and wherein the resultant Gaussian time series data indicates forecasted time series data of the historical time series data.