Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
Applied Intelligence
A study for multi-objective fitness function for time series forecasting with intelligent techniques
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A hybrid method for tuning neural network for time series forecasting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A New Intelligent System Methodology for Time Series Forecasting with Artificial Neural Networks
Neural Processing Letters
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Correcting and combining time series forecasters
Neural Networks
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Forecasting systems have been widely used for decision making and one of its most promising approaches is based on Artificial Neural Networks (ANN). In this paper, a hybrid swarm system is presented for the time series forecasting problem, which consists of an intelligent hybrid model composed of an ANN combined with Particle Swarm Optimizer (PSO). The proposed method searches the relevant time lags for a correct characterization of the time series, as well as the number of processing units in the hidden layer, the training algorithm and the modeling of ANN. The proposed method shows an efficient procedure to adjust the ANN parameters through the use of a particle swarm optimization mechanism. An experimental analysis is conducted with the proposed method using six real world time series and the results are discussed according to five performance measures.