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

作　　者：LIU Shufen GU Songyuan PENG Jun 

机构地区：[1]College of Computer Science and Technology,Jilin University,Changchun 130012,China

出　　处：《Chinese Journal of Electronics》2017年第6期1147-1153,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61472160);the National Key Technology Research and Development Program of China(No.2014BAH29F03)

摘　　要：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 comparative method in all forecasting performance statistics.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 comparative method in all forecasting performance statistics.

关 键 词：Time series Self-adaptivity Unit root Autoregressive moving average(ARMA) 

分 类 号：O211.61[理学—概率论与数理统计]