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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A multivariate heuristic model for fuzzy time-series forecasting.

Kun-Huang Huarng¹, Tiffany Hui-Kuang Yu, Yu Wei Hsu

¹Department of International Trade, Feng Chia University, Taichung 40724, Taiwan, ROC. khhuarng@fcu.edu.tw

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

|August 19, 2007

Summary

This summary is machine-generated.

This study introduces a new multivariate heuristic function to enhance fuzzy time-series models. The method improves forecasting accuracy for nonlinear data while simplifying complex matrix computations.

Related Experiment Videos

Last Updated: Jul 9, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

Artificial Intelligence
Data Science
Time Series Analysis

Background:

Fuzzy time-series models excel at handling nonlinear data without strict assumptions.
These models often outperform traditional forecasting methods.
However, complex matrix computations can be a limitation.

Purpose of the Study:

To propose a novel multivariate heuristic function for fuzzy time-series models.
To integrate this function with univariate models to create effective multivariate models.
To enhance forecasting accuracy and computational efficiency.

Main Methods:

Development of a multivariate heuristic function.
Integration of the heuristic function with existing univariate fuzzy time-series models.
Application to handle multiple variables in forecasting.

Main Results:

The integrated model successfully handles multiple variables.
Forecasting results are improved compared to conventional methods.
Complicated matrix computations are avoided, simplifying the process.

Conclusions:

The proposed multivariate heuristic function offers a computationally efficient approach to fuzzy time-series forecasting.
This method enhances the capability of fuzzy time-series models in handling complex, nonlinear, and multivariate data.
It provides a flexible framework for extending various univariate models.