Walk-forward empirical wavelet random vector functional link for time series forecasting

Ruobin Gao, Liang Du*, Kum Fai Yuen, Ponnuthurai Nagaratnam Suganthan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

The challenge of accurately forecasting a time series covers numerous disciplines, from economics to engineering. Among the thousands of machine learning models, random vector functional link (RVFL) is a robust and efficient model which has demonstrated its success in various challenging forecasting problems. RVFL is an efficient universal function appropriator that randomly generates the weights between the input and hidden layers. However, RVFL still lacks the strong ability to extract meaningful multi-scale features from input data because of the single-layer random mapping of enhancement nodes. Therefore, we propose to combine the empirical wavelet transformation (EWT) with RVFL to strengthen the multi-scale feature extraction ability. The EWT can decompose the original time series into several sub-series which carry the information of different frequencies. Besides, we propose a walk-forward decomposition mechanism to implement the EWT. By introducing such a walk-forward mechanism and the combination of EWT and RVFL, the hybrid model achieves high accuracy and averts the data leakage problem during forecasting. A detailed and comprehensive empirical study on twenty-six public time series validates the proposed model's superiority compared with ten popular baseline models from the literature.

Original languageEnglish
Article number107450
JournalApplied Soft Computing
Volume108
DOIs
Publication statusPublished - Sept 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021

ASJC Scopus Subject Areas

  • Software

Keywords

  • Empirical wavelet transformation
  • Machine learning
  • Online learning
  • Random-vector functional link
  • Time series forecasting

Fingerprint

Dive into the research topics of 'Walk-forward empirical wavelet random vector functional link for time series forecasting'. Together they form a unique fingerprint.

Cite this