Self-Organised Direction Aware Data Partitioning for Type-2 Fuzzy Time Series Prediction

Abstract

Time series forecasting is an essential research field that provides significant data to help professionals in several areas. Thus, growing research and development in this area have been conducted, aiming at developing new forecasting methods with higher performance levels, but always also with low processing costs. One of this methods is Fuzzy Time Series - FTS. However, one great problem of FTS prediction is how to properly deal with the uncertainty associated to the time series and to model s design. Thus, in this paper we propose a univariate interval type-2 fuzzy time series model combined with the concept of Self-organised Direction Aware Data Partitioning Algorithm (SODA) for universe of discourse partitioning. All experiments were performed using the TAIEX data set and the results were then compared to other forecasting models from literature. A sliding window methodology was applied and the forecast error metric chosen was the Root Mean Squared Error (RMSE) for all methods. SODA-T2FTS results show that it outperformed other forecasting methods confirming that interval type-2 fuzzy logic can be a reliable tool for time series prediction.