Self‐adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

As the Box‐Jenkins method could not grasp the non‐stationary characteristics of time series exactly, nor identify the optimal forecasting model order quickly and precisely, a self‐adaptive processing and forecasting algorithm for univariate linear time series is proposed. A self‐adaptive series characteristic test framework which employs varieties of statistic tests is constructed to solve the problem of inaccurate identification and inadequate processing for non‐stationary characteristics of time series. To achieve favorable forecasts, an optimal forecasting model building algorithm combined with model filter and candidate model pool is proposed, in which a univariate linear time series forecasting model is built. Experimental data demonstrates that the proposed algorithm outperforms the comparativemethod in all forecasting performance statistics.