TaSe model for long term time series forecasting

Authors:
Luis Javier Herrera;Héctor Pomares;Ignacio Rojas;Alberto Guillén;Olga Valenzuela;Alberto Prieto
Affiliations:
Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, E.T.S. Computer Engineering, University of Granada, Granada, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

There exists a wide range of paradigms and a high number of different methodologies applied to the problem of Time Series Prediction. Most of them are presented as a modified function approximation problem using I/O data, in which the input data is expanded using outputs at previous steps. Thus the model obtained normally predicts the value of the series at a time (t + h) using previous time steps (t – τ1), (t – τ2),...,(t – τn). Nevertheless, learning a model for long term time series prediction might be seen as a completely different task, since it will generally use its own outputs as inputs for further training, as in recurrent networks. In this paper we present the utility of the TaSe model using the well-known Mackey Glass time series and an approach that upgrades the performance of the TaSe one-step-ahead prediction model for long term prediction.