Building time series forecasting model by independent component analysis mechanism

Building a time series forecasting model by independent component analysis mechanism presents in the paper. Different from using the time series directly with the traditional ARIMA forecasting model, the underlying factors extracted from time series is the forecasting base in our model. Within component ambiguity, correlation approximation and mean difference problems, independent component analysis mechanism has intrinsic limitations for time series forecasting. Solutions for those limitations were purposed in this paper. Under the linear time complexity, the component ambiguity and mean difference problem was solved by our proposed evaluation to improve the forecasting reward. The empirical data show that our model exactly reveals the flexibility and accuracy in time series forecasting domain.