LIU Shufen, GU Songyuan, BAO Tie. An Automatic Forecasting Method for Time Series[J]. Chinese Journal of Electronics, 2017, 26(3): 445-452. doi: 10.1049/cje.2017.01.011
Citation: LIU Shufen, GU Songyuan, BAO Tie. An Automatic Forecasting Method for Time Series[J]. Chinese Journal of Electronics, 2017, 26(3): 445-452. doi: 10.1049/cje.2017.01.011

An Automatic Forecasting Method for Time Series

doi: 10.1049/cje.2017.01.011
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: BAO Tie (corresponding author) was born in Jilin Province, China, in 1978. He received the B.S., M.S. and Ph.D. degrees from Jilin University, China, in 2001, 2004 and 2007, respectively, all in Computer Science and Technology. His research area covers software evaluation and analysis, network management, etc. (Email:apche@126.com)
  • Received Date: 2016-02-01
  • Rev Recd Date: 2016-06-03
  • Publish Date: 2017-05-10
  • An automatic forecasting method is proposed concerning automation problem in the field of linear time series forecasting. The method is on the basis of econometric theory and overcomes the difficulty to mine and forecast automatically with econometric models. The proposed algorithm is divided into 4 stages, i.e. preprocessing, unit root testing and stationary processing, modeling, and ultimately forecasting. Future values and trends would be estimated and forecasted precisely through the 4 stages of the algorithm according to input data without manual intervention. Experimental comparisons were made between the proposed algorithm and the 2 data driven forecasting algorithms, i.e. moving average method and Holt exponential smoothing method. It was demonstrated with the experimental results that automatic forecasting is feasible utilizing the proposed algorithm and higher accuracy can be acquired than these 2 data driven-based methods.
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