Granger causality and time series forecasting
Wang Li-zh · Kongzhi yu juece · 2014
A method of time series forecasting using Granger causality information is presented. Which solves the problem of incomplete information in time series forecasting. Firstly, more available information is determined by correlation test among the system of time series. Then, neural networks are used to abstract the available information. Finally, the obtaining information is integrated in the process of forecasting. Experimental results show the effectiveness and stability of the proposed method.