Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Nonstationarity and Data Preprocessing for Neural Network Predictions of an Economic Time Series
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5 - Volume 5
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
Neuronal implementation of predictive controllers
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
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Time series often exhibit periodical patterns that can be analysed by conventional statistical techniques. These techniques rely upon an appropriate choice of model parameters that are often difficult to determine. Whilst neural networks also require an appropriate parameter configuration, they offer a way in which non-linear patterns may be modelled. However, evidence from a limited number of experiments has been used to argue that periodical patterns cannot be modelled using such networks. In this paper, we present a method to overcome the perceived limitations of this approach by determining the configuration parameters of a time delayed neural network from the seasonal data it is being used to model. Our method uses a fast Fourier transform to calculate the number of input tapped delays, with results demonstrating improved performance as compared to that of other linear and hybrid seasonal modelling techniques.