Comparative study on exponentially weighted moving average approaches for the self-starting forecasting

Jaehong Yu, Seoung Bum Kim, Jinli Bai, Sung Won Han

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, a number of data analysists have suffered from an insufficiency of historical observations in many real situations. To address the insufficiency of historical observations, self-starting forecasting process can be used. A self-starting forecasting process continuously updates the base models as new observations are newly recorded, and it helps to cope with inaccurate prediction caused by the insufficiency of historical observations. This study compared the properties of several exponentially weighted moving average methods as base models for the self-starting forecasting process. Exponentially weighted moving average methods are the most widely used forecasting techniques because of their superior performance as well as computational efficiency. In this study, we compared the performance of a self-starting forecasting process using different existing exponentially weighted moving average methods under various simulation scenarios and real case datasets. Through this study, we can provide the guideline for determining which exponentially weighted moving average method works best for the self-starting forecasting process.

Original languageEnglish
Article number7351
Pages (from-to)1-18
Number of pages18
JournalApplied Sciences (Switzerland)
Volume10
Issue number20
DOIs
Publication statusPublished - 2020 Oct 2

Keywords

  • Comparative study
  • Exponentially weighed moving average
  • Non-stationary time series
  • Self-starting forecasting

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Comparative study on exponentially weighted moving average approaches for the self-starting forecasting'. Together they form a unique fingerprint.

Cite this