Fuzzy forecasting based on fuzzy-trend logical relationship groups

Authors:
Shyi-Ming Chen;Nai-Yi Wang
Affiliations:
Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2010

Citing 12
Cited 2

Fuzzy time series and its models

Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I

Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II

Fuzzy Sets and Systems
A comparison of fuzzy forecasting and Markov modeling

Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series

Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series

Fuzzy Sets and Systems
A new method to estimate null values in relational database systems based on automatic clustering techniques

Information Sciences: an International Journal
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles

International Journal of Intelligent Systems
Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series

Expert Systems with Applications: An International Journal
Temperature prediction using fuzzy time series

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ratio-based lengths of intervals to improve fuzzy time series forecasting

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling forecasting problems based on two-factors high-order fuzzy time series

IEEE Transactions on Fuzzy Systems

TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines

Information Sciences: an International Journal
Determination of temporal information granules to improve forecasting in fuzzy time series

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.02

Visualization

Abstract

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.