Constructing Fuzzy Time Series Model Based on Fuzzy Clustering for a Forecasting

Abstract

Problem statement: In this study researchers introduced a fuzzy time series model depending on fuzzy clustering to solve the problem in which the membership values are assumed as Song and Chissom model and to increase the performance of fuzzy time series model. Approach: Proposed model employed seven main procedures in time-invariant fuzzy time-series and time-variant fuzzy time series models. In the first step: clustering data, in the second step: determine membership values for each cluster, the third step: define the universe of discourse, in the fourth step: partition universal of discourse into equal intervals, in the fifth step: fuzzify the historical data, in the sixth step: build fuzzy logic relationships and the last step: calculate forecasted outputs to increase the performance of the proposed fuzzy time series model. Results: From the evaluations, the proposed model can further improve the forecasting results than the other model. Conclusion: The proposed model is a good model for forecasting values. Selecting membership functions based on fuzzy clustering offers an alternative approach to let the data determine the nature of the membership functions. Our results showed that this approach can lead to satisfactory performance for fuzzy time series.