A Hybrid Time Series Prediction Model Based on Fuzzy Time Series and Maximal Overlap Discrete Wavelet Transform

Abstract

This study proposes a new time series prediction method that combines Fuzzy Time Series (FTS) based on fuzzy clustering and Maximal Overlap Discrete Wavelet Transform (MODWT). Time series generally consist of subseries, each of which reflects the different behavior of the time series and using of a single prediction method for all subseries can be negatively impacted the prediction and forecasting accuracy. Proposed method is based on decomposing of time series into sub-time series through MODWT and predicting an FTS model for each sub-time series separately. Besides, time series can contain noise, outlier or unwanted data points and these points can hide the actual behavior of the time series. MODWT has the ability of eliminating negative effects of these kind of data points on the predictions. Besides, proposed method has also all advantages of FTS methods. The main objective of this study based on these advantages is to improve the prediction and forecasting performance of existing FTS methods based on fuzzy clustering. In order to show the performance of proposed method, three FTS methods based on fuzzy clustering and wavelet-based versions of them are applied to eight real time series and experimental results clearly showed that proposed method achieves the best prediction and forecasting results.

Keywords

• Fuzzy clustering
• Fuzzy time series
• Wavelet decomposition
• Maximal overlap discrete wavelet decomposition

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