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
Fuzzy time-series based on adaptive expectation model for TAIEX forecasting
Expert Systems with Applications: An International Journal
Handling forecasting problems based on two-factors high-order fuzzy time series
IEEE Transactions on Fuzzy Systems
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
A fuzzy time series-based neural network approach to option price forecasting
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
Forecasting short-term trends of stock markets based on fuzzy frequent pattern tree
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A generalized method for forecasting based on fuzzy time series
Expert Systems with Applications: An International Journal
Multivariate fuzzy forecasting based on fuzzy time series and automatic clustering techniques
Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction
Expert Systems with Applications: An International Journal
Neural fuzzy forecasting of the china yuan to US dollar exchange rate: a swarm intelligence approach
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Expert Systems with Applications: An International Journal
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
A Critical Evaluation of Computational Methods of Forecasting Based on Fuzzy Time Series
International Journal of Decision Support System Technology
Hi-index | 12.06 |
Fuzzy time series model has been successfully employed in predicting stock prices and foreign exchange rates. In this paper, we propose a new fuzzy time series model termed as distance-based fuzzy time series (DBFTS) to predict the exchange rate. Unlike the existing fuzzy time series models which require exact match of the fuzzy logic relationships (FLRs), the distance-based fuzzy time series model uses the distance between two FLRs in selecting prediction rules. To predict the exchange rate, a two factors distance-based fuzzy time series model is constructed. The first factor of the model is the exchange rate itself and the second factor comprises many candidate variables affecting the fluctuation of exchange rates. Using the exchange rate data released by the Central Bank of Taiwan, we conducted several experiments on exchange rate forecasting. The experiment results showed that the distance-based fuzzy time series outperformed the random walk model and the artificial neural network model in terms of mean square error.