Fuzzy time series and its models
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part I
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
An improved fuzzy time series forecasting method using trapezoidal fuzzy numbers
Fuzzy Optimization and Decision Making
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
Fuzzy dual-factor time-series for stock index forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
The aim of this paper is to improve the fuzzy logical forecasting model (FILF) by utilizing multivariate inference and the partitioning problem for an exponentially distributed time series by using a multiplicative clustering approach. Fuzzy time series (FTS) is a growing study field in computer science and its superiority is indicated frequently. Since the conventional time series analysis requires various pre-conditions, the FTS framework is very useful and convenient for many problems in business practice. This paper particularly investigates pricing problems in the shipping business and price-volatility relationship is the theoretical point of the proposed approach. Both FTS and conventional time series results are comparatively presented in the final section and superiority of the proposed method is explicitly noted.