Introduction to artificial neural systems
Introduction to artificial neural systems
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
A new fuzzy time-series model of fuzzy number observations
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
Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles
International Journal of Intelligent Systems
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting
Intelligent Data Analysis
The clustering algorithm for nonlinear system identification
WSEAS Transactions on Computers
WSEAS Transactions on Computers
Expert Systems with Applications: An International Journal
Optimal adaptive fuzzy control for a class of unknown nonlinear systems
WSEAS Transactions on Systems and Control
Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations
Expert Systems with Applications: An International Journal
WSEAS TRANSACTIONS on SYSTEMS
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
A Multivariate Heuristic Model for 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
Fuzzy time series model incorporating predictor variables and interval partition
WSEAS Transactions on Mathematics
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The problem of fuzzy time series forecasting plays an important role in many scientific areas such as statistics and neural networks. While forecasting fuzzy time series, most of forecasting applications use the same length of intervals. The determination of length of intervals is significant and critical in fuzzy time series forecasting. The usage of convenient performance measure may also have an important affect for forecasting studies. MSE (Mean squared error) as a performance measure is widely used in many studies. The aim of this paper is to improve fuzzy time series forecasting by using different length of intervals with neural networks according to various performance measures. For this reason, we take ISE (Istanbul stock exchange) national-100 index as a large data set for forecasting. We use various performance measures such as MSE, RMSE (Root mean squared error), MAE (Mean absolute error) and MAPE (Mean absolute percentage error) to compare forecasting performances with different length of intervals. The empirical results show that the most convenient length of intervals can be chosen as 300 by comparing overall performance of MSE, RMSE, MAE and MAPE by using neural networks.