A high-order Yule-Walker method for estimation of the AR parameters of an ARMA model
Systems & Control Letters
Performance of neural networks in managerial forecasting
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
Statistical analysis of the main parameters: in the fuzzy inference process
Fuzzy Sets and Systems
Model selection in neural networks
Neural Networks
Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks
Neural Processing Letters
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
TaSe model for long term time series forecasting
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
Pruning recurrent neural networks for improved generalization performance
IEEE Transactions on Neural Networks
Machine condition prognosis based on sequential Monte Carlo method
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Intelligent forecasting of S&P 500 time series: a self-organizing fuzzy approach
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting
Engineering Applications of Artificial Intelligence
Differential evolution-based nonlinear system modeling using a bilinear series model
Applied Soft Computing
A dual hybrid forecasting model for support of decision making in healthcare management
Advances in Engineering Software
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The challenge of predicting future values of a time series covers a variety of disciplines. The fundamental problem of selecting the order and identifying the time varying parameters of an autoregressive moving average model (ARMA) concerns many important fields of interest such as linear prediction, system identification and spectral analysis. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. This study was designed: (a) to investigate a hybrid methodology that combines ANN and ARMA models; (b) to resolve one of the most important problems in time series using ARMA structure and Box-Jenkins methodology: the identification of the model. In this paper, we present a new procedure to predict time series using paradigms such as: fuzzy systems, neural networks and evolutionary algorithms. Our goal is to obtain an expert system based on paradigms of artificial intelligence, so that the linear model can be identified automatically, without the need of human expert participation. The obtained linear model will be combined with ANN, making up an hybrid system that could outperform the forecasting result.