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Journal of Computer Science and Technology

, Volume 34, Issue 2, pp 318–338 | Cite as

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

  • Feng ZhouEmail author
  • Hao-Min Zhou
  • Zhi-Hua Yang
  • Li-Hua Yang
Regular Paper
  • 6 Downloads

Abstract

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 non-stationary 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).

Keywords

iterative filtering factorization machine neural network time series data recovery 

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Copyright information

© Springer Science+Business Media, LLC & Science Press, China 2019

Authors and Affiliations

  • Feng Zhou
    • 1
    Email author
  • Hao-Min Zhou
    • 2
  • Zhi-Hua Yang
    • 1
  • Li-Hua Yang
    • 3
    • 4
  1. 1.School of Information ScienceGuangdong University of Finance and EconomicsGuangzhouChina
  2. 2.School of MathematicsGeorgia Institute of TechnologyAtlantaU.S.A.
  3. 3.Guangdong Province Key Laboratory of Computational ScienceGuangzhouChina
  4. 4.School of MathematicsSun Yat-sen UniversityGuangzhouChina

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