LIU Shufen, GU Songyuan, PENG Jun. Self-adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series[J]. Chinese Journal of Electronics, 2017, 26(6): 1147-1153. doi: 10.1049/cje.2017.09.027
Citation: LIU Shufen, GU Songyuan, PENG Jun. Self-adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series[J]. Chinese Journal of Electronics, 2017, 26(6): 1147-1153. doi: 10.1049/cje.2017.09.027

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

doi: 10.1049/cje.2017.09.027
Funds:  This work is supported by the National Natural Science Foundation of China (No.61472160), and the National Key Technology Research and Development Program of China (No.2014BAH29F03).
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  • Corresponding author: PENG Jun (corresponding author) was born in Chongqing, China in 1981. He received the Ph.D. degree in computer science and technology from Jilin University in 2010. Currently he is a lecturer of College of Computer Science and Technology in Jilin University.(Email:pengjun@mail.jlu.edu.cn)
  • Received Date: 2017-05-11
  • Rev Recd Date: 2017-06-24
  • Publish Date: 2017-11-10
  • 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.
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