The Hybrids Algorithm Based on Fuzzy Cognitive Map for Fuzzy Time Series Prediction

Abstract

Fuzzy logic relationship groups are crucial to fuzzy time series prediction. In generally, fuzzy logic relationship groups can be constructed by manually mining fuzzy logic relationship between adjacent data in time series. However, for the large-scale or long-term time series, the way of manually constructing fuzzy logical relationship groups is difficult and infeasible. In this paper, a hybrids prediction algorithm based on Fuzzy Cognitive Map (FCM) is proposed, in which fuzzy c-means clustering algorithm is used to construct the framework of FCM and genetic algorithm (GA) is applied to learn weights of FCM. Finally, a fully learned fuzzy cognitive map is used to represent, store fuzzy logic relationships of fuzzy time series and realize prediction. A benchmark time series — the enrollments of University of Alberta time series is applied to validate the feasibility and effectiveness of the proposed algorithm, whose results show that the proposed prediction algorithm based on FCM is effective and can obtain the satisfactory prediction precision. It is a potential virtue that the proposed algorithm can automatically process the prediction problem of the large-scale or long-term time series.