Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neural network models for time series forecasts
Management Science
Real-valued genetic algorithms for fuzzy grey prediction system
Fuzzy Sets and Systems
Statistical analysis of the main parameters: in the fuzzy inference process
Fuzzy Sets and Systems
Analysis of the Functional Block Involved in the Design of Radial Basis Function Networks
Neural Processing Letters
An ARMA order selection method with fuzzy reasoning
Signal Processing - Special section on information theoretic aspects of digital watermarking
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Fitting ARMA Models to linear non-Gaussian processes using higher order statistics
Signal Processing - Image and Video Coding beyond Standards
Building ARMA Models with Genetic Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Evolving Time Series Forecasting ARMA Models
Journal of Heuristics
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Structure identification in complete rule-based fuzzy systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Predicting sun spots using a layered perceptron neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Application of Radial Basis Function Neural Network for Sales Forecasting
CAR '09 Proceedings of the 2009 International Asia Conference on Informatics in Control, Automation and Robotics
A Fuzzy Asymmetric GARCH model applied to stock markets
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
ARIMA Model Estimated by Particle Swarm Optimization Algorithm for Consumer Price Index Forecasting
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling
Information Sciences: an International Journal
Fuzzy time series prediction using hierarchical clustering algorithms
Expert Systems with Applications: An International Journal
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
Information Sciences: an International Journal
A new hybrid methodology for nonlinear time series forecasting
Modelling and Simulation in Engineering
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
Nonlinear time series modeling and prediction using local variable weights RBF network
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Engineering Applications of Artificial Intelligence
An Evolutionary Functional Link Neural Fuzzy Model for Financial Time Series Forecasting
International Journal of Applied Evolutionary Computation
Hi-index | 0.21 |
Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. 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. In this paper we propose a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA-ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of the ARMA or ARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA-ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs.