Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (2): 318-338.doi: 10.1007/s11390-019-1913-0

• Special Section of Advances in Computer Science and Technology—Current Advances in the NSFC Joint Research Fund for Overseas Chinese Scholars and Scholars in Hong Kong and Macao 2014-2017 (Part 2) • Previous Articles     Next Articles

A 2-Stage Strategy for Non-Stationary Signal Prediction and Recovery Using Iterative Filtering and Neural Network

Feng Zhou1, Hao-Min Zhou2, Zhi-Hua Yang1, Li-Hua Yang3,4, Senior Member, IEEE   

  1. 1 School of Information Science, Guangdong University of Finance and Economics, Guangzhou 510320, China;
    2 School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.;
    3 Guangdong Province Key Laboratory of Computational Science, Guangzhou 510275, China;
    4 School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
  • Received:2018-10-14 Revised:2019-01-18 Online:2019-03-05 Published:2019-03-16
  • About author:Feng Zhou received his B.S. degree in information computing science from Minnan Normal University, Zhangzhou, in 2010, and Ph.D. degree in computational mathematics from Sun Yat-sen University, Guangzhou, in 2015. As a visiting student, he studied in the School of Mathematics, Georgia Institute of Technology, Atlanta, from 2013 to 2014, supported by the China Scholarship Council (CSC). From 2015 to 2017, he worked as a sensor algorithm engineer in Baidu and Tencent successively in the research of recommendation system. Now he is an assistant professor at the School of Information Science, Guangdong University of Finance and Economics, Guangzhou. His research interests include signal analysis, machine learning and ensemble learning.
  • Supported by:
    This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 11771458, 431015 and 61628203, the National Science Foundation of US under Grant Nos. DMS-1620345 and DMS-1830225, the Office of Naval Research (ONR) Award of US under Grant No. N00014-18-1-2852, the Guangdong Youth Innovation Talent Project (Natural Sciences) under Grant No. 2017KQNCX083, the Guangdong Philosophy and Social Science Project of China under Grant No. GD15CGL11, and the Guangzhou Science and Technology Project of China under Grant No. 201707010495.

Predicting the future information and recovering the missing data for time series are two vital tasks faced in various application fields. They are often subjected to big challenges, especially when the signal is nonlinear and nonstationary which is common in practice. In this paper, we propose a hybrid 2-stage approach, named IF2FNN, to predict (including short-term and long-term predictions) and recover the general types of time series. In the first stage, we decompose the original non-stationary series into several “quasi stationary” intrinsic mode functions (IMFs) by the iterative filtering (IF) method. In the second stage, all of the IMFs are fed as the inputs to the factorization machine based neural network model to perform the prediction and recovery. We test the strategy on five datasets including an artificial constructed signal (ACS), and four real-world signals: the length of day (LOD), the northern hemisphere land-ocean temperature index (NHLTI), the troposphere monthly mean temperature (TMMT), and the national association of securities dealers automated quotations index (NASDAQ). The results are compared with those obtained from the other prevailing methods. Our experiments indicate that under the same conditions, the proposed method outperforms the others for prediction and recovery according to various metrics such as mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE).

Key words: iterative filtering, factorization machine, neural network, time series, data recovery

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