Fuzzy data analysis by possibilistic linear models
Fuzzy Sets and Systems - Fuzzy Numbers
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Generalization and parameter estimation in feedforward nets: some experiments
Advances in neural information processing systems 2
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
Computers and Operations Research
Forecasting S&P 500 stock index futures with a hybrid AI system
Decision Support Systems
A comparison between neural networks and chaotic models for exchange rate prediction
Computational Statistics & Data Analysis
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
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
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
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
Hybrid neural network models for hydrologic time series forecasting
Applied Soft Computing
Optimizing feedforward artificial neural network architecture
Engineering Applications of Artificial Intelligence
Forecasting nonlinear time series with neural network sieve bootstrap
Computational Statistics & Data Analysis
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A new hybrid methodology for nonlinear time series forecasting
Modelling and Simulation in Engineering
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Computers and Industrial Engineering
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting
Engineering Applications of Artificial Intelligence
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
International Journal of Applied Evolutionary Computation
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
Fuzzy artificial neural network p, d, q model for incomplete financial time series forecasting
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Time series forecasting is an active research area that has drawn considerable attention for applications in a variety of areas. Auto-Regressive Integrated Moving Average (ARIMA) models are one of the most important time series models used in financial market forecasting over the past three decades. Recent research activities in time series forecasting indicate that two basic limitations detract from their popularity for financial time series forecasting: (a) ARIMA models assume that future values of a time series have a linear relationship with current and past values as well as with white noise, so approximations by ARIMA models may not be adequate for complex nonlinear problems; and (b) ARIMA models require a large amount of historical data in order to produce accurate results. Both theoretical and empirical findings have suggested that integration of different models can be an effective method of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, ARIMA models are integrated with Artificial Neural Networks (ANNs) and Fuzzy logic in order to overcome the linear and data limitations of ARIMA models, thus obtaining more accurate results. Empirical results of financial markets forecasting indicate that the hybrid models exhibit effectively improved forecasting accuracy so that the model proposed can be used as an alternative to financial market forecasting tools.