Fuzzy Time Series Forecasting Accuracy Based on Hybrid Similarity Measure

Abstract

The majority of fuzzy time series forecasting (FTSF) algorithms assess forecasting accuracy using an error-based distance. The predicted value is defuzzified to a crisp number and the error-based distance will be computed. Defuzzification causes some information to be lost, which leads to its inability to comprehend the level of uncertainty that has been preserved during the forecasting process. This paper proposes an enhanced FTSF model with forecasting accuracy developed based on a new hybrid similarity measure combining the centre of gravity and area and height. Three properties of the hybrid similarity measure are presented. The FTSF model is implemented in the case of the Malaysian unemployment rate. The findings indicate that, on average more than 94% of the predicted value was identical to historical data. The forecasting accuracy is produced directly from the forecasting value without undergoing the defuzzification process, which can preserve some information from being lost.