Prediction method of autoregressive moving average models for uncertain time series

Jingwen Lu, Jin Peng, Jinyang Chen, Kiki Ariyanti Sugeng

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Time series analysis is based on the continuous regularity of the development of objective things to predict the next value depending on observed values. Based on time series analysis, we present autoregressive moving average models to predict the next future value for an uncertain time series. In this paper, imprecise observations and disturbance terms are regarded as uncertain variables and assume that the latter are satisfied uncertain normal distribution. The prediction models of uncertain time series are established combining the knowledge of autoregressive model and uncertainty theory. Therefore, the interval range of the next future value is predicted based on the reliability constraint. As an illustration to compare with the numerical examples of the existing prediction method, the innovations and effectiveness of the work are further demonstrated by the computational results.

Original languageEnglish
Pages (from-to)546-572
Number of pages27
JournalInternational Journal of General Systems
Volume49
Issue number5
DOIs
Publication statusPublished - 3 Jul 2020

Keywords

  • autoregressive moving average models
  • prediction method
  • uncertain time series
  • Uncertainty theory

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