Constructing Fuzzy Time Series Model Using Combination of Table Lookup and Singular Value Decomposition Methods and Its Application to Forecasting Inflation Rate

Abstract

Fuzzy time series is a dynamic process with linguistic values as its observations. Modelling fuzzy time series data developed by some researchers used discrete membership functions and table lookup method from training data. This paper presents a new method to modelling fuzzy time series data combining table lookup and singular value decomposition methods using continuous membership functions. Table lookup method is used to construct fuzzy relations from training data. Singular value decomposition of firing strength matrix and QR factorization are used to reduce fuzzy relations. Furthermore, this method is applied to forecast inflation rate in Indonesia based on six-factors one-order fuzzy time series. This result is compared with neural network method and the proposed method gets a higher forecasting accuracy rate than the neural network method.