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Related Experiment Videos

A stochastic HMM-based forecasting model for fuzzy time series.

Sheng-Tun Li¹, Yi-Chung Cheng

¹Institute of Information Management and the Department of Industrial and Information Management, National Cheng Kung University, Tainan 701, Taiwan. stli@mail.ncku.edu.tw

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|December 24, 2009

Summary

This study introduces a novel fuzzy time series forecasting model using a hidden Markov model to handle complex two-factor problems. The enhanced model improves accuracy and statistical approximation for real-world data, like weather and stock exchange rates.

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Area of Science:

Time Series Analysis
Fuzzy Logic
Stochastic Modeling

Background:

Traditional time series methods struggle with data uncertainty.
Existing fuzzy time series models often use IF-THEN rules, causing computational inefficiency and redundancy.
Previous Markov-based models were limited to single-factor forecasting.

Purpose of the Study:

To propose a novel fuzzy time series forecasting model.
To enhance existing Markov-based models for two-factor forecasting.
To improve the realism of forecasting by incorporating randomness and uncertainty.

Main Methods:

Developed a novel forecasting model based on the hidden Markov model.
Extended Sullivan and Woodall's work to accommodate two-factor forecasting.
Utilized the Monte Carlo method for outcome estimation and incorporated the central limit theorem.

Main Results:

The proposed model demonstrated improved forecasting accuracy in experiments.
Successfully handled two-factor forecasting problems, unlike previous methods.
Results statistically approximated the real mean of target values, adhering to the central limit theorem.

Conclusions:

The novel hidden Markov model-based fuzzy time series approach effectively addresses limitations of existing methods.
The model offers enhanced accuracy and statistical validity for complex forecasting tasks.
This research provides a more robust tool for analyzing uncertain and vague time series data.