A new approach for determining the length of intervals for fuzzy time series

Authors:
Ufuk Yolcu;Erol Egrioglu;Vedide R. Uslu;Murat A. Basaran;Cagdas H. Aladag
Affiliations:
Department of Statistics, University of Ondokuz Mayıs, Samsun 55139, Turkey;Department of Statistics, University of Ondokuz Mayıs, Samsun 55139, Turkey;Department of Statistics, University of Ondokuz Mayıs, Samsun 55139, Turkey;Department of Mathematics, Nigde University, Nigde 51200, Turkey;Department of Statistics, University of Hacettepe, Ankara 06800, Turkey
Venue:
Applied Soft Computing
Year:
2009

Citing 7
Cited 9

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
Forecasting enrollments based on fuzzy time series

Fuzzy Sets and Systems
Handling forecasting problems using fuzzy time series

Fuzzy Sets and Systems
A dynamic approach to adjusting lengths of intervals in fuzzy time series forecasting

Intelligent Data Analysis
Ratio-based lengths of intervals to improve fuzzy time series forecasting

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A vector forecasting model for fuzzy time series

Applied Soft Computing
Fuzzy time series forecasting method based on Gustafson-Kessel fuzzy clustering

Expert Systems with Applications: An International Journal
A generalized method for forecasting based on fuzzy time series

Expert Systems with Applications: An International Journal
Forecasting shanghai composite index based on fuzzy time series and improved C-fuzzy decision trees

Expert Systems with Applications: An International Journal
A new time invariant fuzzy time series forecasting method based on particle swarm optimization

Applied Soft Computing
Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks

Expert Systems with Applications: An International Journal
Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach

Expert Systems with Applications: An International Journal
Determination of temporal information granules to improve forecasting in fuzzy time series

Expert Systems with Applications: An International Journal
Introducing polynomial fuzzy time series

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.02

Visualization

Abstract

In the implementations of fuzzy time series forecasting, the identification of interval lengths has an important impact on the performance of the procedure. However, the interval length has been chosen arbitrarily in many papers. Huarng developed a new approach which is called ratio-based lengths of intervals in order to identify the length of intervals. In our paper, we propose a new approach which uses a single-variable constrained optimization to determine the ratio for the length of intervals. The proposed approach is applied to the two well-known time series, which are enrollment data at The University of Alabama and inventory demand data. The obtained results are compared to those of other methods. The proposed method produces more accurate predictions for the future values of used time series.