A stochastic HMM-based forecasting model for fuzzy time series

Sheng Tun Li, Yi Chung Cheng

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

Recently, fuzzy time series have attracted more academic attention than traditional time series due to their capability of dealing with the uncertainty and vagueness inherent in the data collected. The formulation of fuzzy relations is one of the key issues affecting forecasting results. Most of the present works adopt IFTHEN rules for relationship representation, which leads to higher computational overhead and rule redundancy. Sullivan and Woodall proposed a Markov-based formulation and a forecasting model to reduce computational overhead; however, its applicability is limited to handling one-factor problems. In this paper, we propose a novel forecasting model based on the hidden Markov model by enhancing Sullivan and Woodall's work to allow handling of two-factor forecasting problems. Moreover, in order to make the nature of conjecture and randomness of forecasting more realistic, the Monte Carlo method is adopted to estimate the outcome. To test the effectiveness of the resulting stochastic model, we conduct two experiments and compare the results with those from other models. The first experiment consists of forecasting the daily average temperature and cloud density in Taipei, Taiwan, and the second experiment is based on the Taiwan Weighted Stock Index by forecasting the exchange rate of the New Taiwan dollar against the U.S. dollar. In addition to improving forecasting accuracy, the proposed model adheres to the central limit theorem, and thus, the result statistically approximates to the real mean of the target value being forecast.

Original languageEnglish
Article number5356151
Pages (from-to)1255-1266
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number5
DOIs
Publication statusPublished - 2010 Oct 1

Fingerprint

Time series
Taiwan
Monte Carlo Method
Experiments
Stochastic models
Hidden Markov models
Uncertainty
Redundancy
Monte Carlo methods
Temperature

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

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A stochastic HMM-based forecasting model for fuzzy time series. / Li, Sheng Tun; Cheng, Yi Chung.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 40, No. 5, 5356151, 01.10.2010, p. 1255-1266.

Research output: Contribution to journalArticle

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