Forecasting enrollments with fuzzy time series—part I
Fuzzy Sets and Systems
Forecasting enrollments with fuzzy time series—part II
Fuzzy Sets and Systems
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
A fuzzy seasonal ARIMA model for forecasting
Fuzzy Sets and Systems - Information processing
A bivariate fuzzy time series model to forecast the TAIEX
Expert Systems with Applications: An International Journal
Fuzzy dual-factor time-series for stock index forecasting
Expert Systems with Applications: An International Journal
A new approach for determining the length of intervals for fuzzy time series
Applied Soft Computing
Finding an optimal interval length in high order fuzzy time series
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Fuzzy time series forecasting (FTSF) is a useful tool for forecasting without expert consultation as well as a user-friendly solution for non-expert forecasters. Before selecting the proper forecasting model, analysis of data series is a key step in the implementation of fuzzy time series forecasting. Seasonality is one of the change-making dimensions of data series that also include temperature, rainfall, freight rates and vessel traffic. The aim of this paper is to improve the fuzzy integrated logical forecasting (FILF) model for the seasonal time series by using the bivariate fuzzy time series approach. The proposed model is applied on the volume of vessel traffic on the Istanbul Strait in order to compare the accuracy of the proposed model with benchmark methods. In addition, the histogram damping partition (HDP) is used to define the initial length of intervals for the fuzzy C-means clustering method.