A Novel FCM-Based Deterministic Forecasting Model for Fuzzy Time Series
Liao Jian-pin · Computer Engineering and Science · 2010
The study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling uncertainty and vagueness in data sampling.There have been a variety of models developed to either improve forecasting accuracy or reduce computation overhead.However,the issues of controlling uncertainty in forecasting,effectively partitioning intervals,and consistently achieving forecasting accrucy with different interval lengths have been rarely investigated.In this paper,a novel forecasting model is proposed,because of the disadvantages of other existing forecasting models.A novel forecasting model enhances forecasting functionality and allows the processing of two-factor forecasting problems.In addition,this model applies fuzzy C-means(FCM) clustering to deal with interval partitioning,which takes the nature of data points into account and produces unequal-sized intervals.The superior accuracy of the proposed model is demonstrated by experiments by comparing it to other existing models using real-world empirical data.