Forecasting Approach in Fuzzy Time Series Based on Information Granules

Aiming at a large number of ambiguous, imprecise and incomplete data in the real world, fuzzy time series has come into being and developed into an effective forecasting approach. In the process of modeling and forecasting of fuzzy time series, the prediction performance of fuzzy time series can be effectively improved by partitioning the universe of discourse into different lengths. In this paper, a forecasting approach for fuzzy time series, which introduces the granularity mechanism into interval division and employs differential data for incremental forecasting, is proposed to solve the problem of time series forecasting with high forecasting precision. In the proposed approach, in order to describe the fuzzy logic relationship and fuzzy trend of historical data, we first do differential processing on the historical samples. Then, Fuzzy C-means (FCM) clustering algorithm is used to generate several partition intervals tentatively. In the sequel, we use the principle of justifiable granularity to constantly adjust the width of all the intervals, so that these information granules associated with corresponding intervals become the most "informative" information granules. Finally, the boundary of information granules is used as the basis of interval division to complete the forecasting task. An illustrative example is provided to demonstrate the essence of the proposed approach. The comparative experiment with other representative approaches shows that the proposed approach can significantly improve the prediction accuracy of time series.