Bagging ensemble-based novel data generation method for univariate time series forecasting

Research output: Contribution to journalArticlepeer-review

Abstract

The most critical issue in time series data is predicting future data values. Recently, an ensemble model combining multiple models with superior predictive performance has emerged. However, in the case of univariate time series data, an accurate prediction remains difficult because of the unique characteristic of the data: there is only one variable to analyze. In this paper, we propose a method to improve the performance of predictive models with a simple structure and apply it to time series data. This study proposes a time series forecasting method based on a bagging ensemble that uses the maximum overlap discrete wavelet transform (MODWT) and bootstrap. The proposed method decomposes the scale and detail of the time series data using the MODWT. The bootstrap is applied to univariate time series to generate bootstrapped data that slightly differ from the characteristics of the original data. Through experiments, we examined the results and validated the details of the proposed method depending on whether the proposed method was applied. In most cases, we confirmed that our proposed method improves the performance of the existing algorithms by employing a nonparametric test. The results show that the performance improved more when the algorithm is simple.

Original languageEnglish
Article number117366
JournalExpert Systems With Applications
Volume203
DOIs
Publication statusPublished - 2022 Oct 1

Keywords

  • Bagging
  • Data augmentation
  • Ensemble method
  • Maximum overlap discrete wavelet transform
  • Neural network
  • Time series forecasting

ASJC Scopus subject areas

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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