ARIMA model estimated by particle swarm optimization algorithm for consumer price index forecasting

Hongjie Wang, Weigang Zhao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)

Abstract

This paper presents an ARIMA model which uses particle swarm optimization algorithm (PSO) for model estimation. Because the traditional estimation method is complex and may obtain very bad results, PSO which can be implemented with ease and has a powerful optimizing performance is employed to optimize the coefficients of ARIMA. In recent years, inflation and deflation plague the world moreover the consumer price index (CPI) which is a measure of the average price of consumer goods and services purchased by households is usually observed as an important indicator of the level of inflation, so the forecast of CPI has been focused on by both scientific community and relevant authorities. Furthermore, taking the forecast of CPI as a case, we illustrate the improvement of accuracy and efficiency of the new method and the result shows it is predominant in forecasting.

Original languageEnglish
Title of host publicationArtificial Intelligence and Computational Intelligence - International Conference, AICI 2009, Proceedings
Pages48-58
Number of pages11
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventInternational Conference on Artificial Intelligence and Computational Intelligence, AICI 2009 - Shanghai, China
Duration: 7 Nov 20098 Nov 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5855 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Country/TerritoryChina
CityShanghai
Period7/11/098/11/09

Keywords

  • ARIMA model
  • Consumer price index
  • Moment estimation
  • Particle swarm optimization algorithm

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