Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
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
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
Handling forecasting problems using fuzzy time series
Fuzzy Sets and Systems
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Computers and Operations Research - Special issue: Emerging economics
A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
A hybrid genetic-neural architecture for stock indexes forecasting
Information Sciences: an International Journal - Special issue: Computational intelligence in economics and finance
2005 Special Issue: A comparative study of autoregressive neural network hybrids
Neural Networks - 2005 Special issue: IJCNN 2005
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
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid SARIMA wavelet transform method for sales forecasting
Decision Support Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A hybrid modeling approach for forecasting the volatility of S&P 500 index return
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
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Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the hybridization are quite different. In the literature, several hybrid techniques have been proposed by combining linear and nonlinear models, in order to overcome the deficiencies of single models and yield results that are more accurate. However, recent research activities in hybrid linear and nonlinear models indicate that these models have two basic limitations that have decreased their popularity for time series forecasting. These two basic limitations are: a the hybrid linear and nonlinear models have some assumptions that will degenerate their performance if the opposite situations occur, and b the hybrid linear and nonlinear models require a large amount of historical data in order to produce accurate results. In this paper, a novel hybrid model is proposed for time series forecasting by combining linear autoregressive integrated moving average ARIMA, nonlinear artificial neural networks ANNs, and fuzzy models. In the proposed model, no prior assumption of traditional hybrid linear and nonlinear models is considered for the relationship between the linear and nonlinear components. In the proposed model the data limitation of traditional hybrid linear and nonlinear models is also lifted through investing on the advantages of the fuzzy models. Empirical results of financial markets, especially exchange rate market, forecasting indicate that proposed model performs significantly better than its components used separately, traditional hybrid linear and nonlinear, and other fuzzy and nonfuzzy models in incomplete data situations.