Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors

Abstract

In this paper, we propose a new fuzzy time series (FTS) forecasting method based on optimal partitions of intervals in the universe of discourse and optimal weighting vectors of two-factors second-order fuzzy-trend logical relationship groups (TSFTLRGs). The proposed method uses particle swarm optimization (PSO) techniques to obtain the optimal partitions of intervals and the optimal weighting vectors simultaneously. The proposed FTS forecasting method outperforms the existing methods for forecasting the TAIEX and the NTD/USD exchange rates in terms of forecasting accuracy rates. It provides us with a useful way to deal with forecasting problems to get higher forecasting accuracy rates.