Abstract
Prediction of time series is an important problem in many areas of science and engineering. Extending the horizon of predictions further to the future is the challenging and difficult task of long-term prediction. In this paper, we investigate the problem of selecting non-contiguous input variables for an autoregressive prediction model in order to improve the prediction ability. We present an algorithm in the spirit of backward selection which removes variables sequentially from the prediction models based on the significance of the individual regressors. We successfully test the algorithm with a non-linear system by selecting inputs with a linear model and finally train a non-linear predictor with the selected variables on Santa Fe laser data set.
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© 2005 Springer-Verlag Berlin Heidelberg
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Tikka, J., Hollmén, J., Lendasse, A. (2005). Input Selection for Long-Term Prediction of Time Series. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_123
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DOI: https://doi.org/10.1007/11494669_123
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
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