Interval-similarity based fuzzy time series forecasting algorithm

Abstract

There are limitations in establishing fuzzy logical relationship of the existing fuzzy time series forecasting methods, which makes it hard to adapt to the appearance of new relationship. In order to overcome the defects, an intervalsimilarity based fuzzy time series forecasting( ISFTS) algorithm was proposed. Firstly, based on fuzzy theory, an averagebased method was used to redivide the intervals of the universe of discourse. Secondly, the fuzzy sets were defined and the historical data were fuzzified, then the third-order fuzzy logical relationships were established and a formula was used to measure the similarity between logical relationships. By computing the changing trend of future data, the fuzzy values were obtained. Finally, the fuzzy values were defuzzified and the forecasting values were obtained. The proposed algorithm makes up for the shortcomings in logical relationship of the existing forecasting algorithms because it forecasts the changing trend of future data. The experimental results show that the proposed algorithm ISFTS is superior to other forecasting algorithms on forecasting error, including Mean Absolute Percentage Error( MAPE), Mean Absolute Error( MAE) and Root Mean Squared Error( RMSE). Therefore, the algorithm ISFTS is more adaptive in time series forecasting, especially in the case of large data.