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 – τ 1), (t – τ 2),...,(t – τ n ). 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.
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References
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modelling and control. IEEE Trans. Syst. Man and Cyber. 15, 116–132 (1985)
Mackey, M.C., Glass, L.: Oscillation and chaos in physcological controlsystems. Science 197, 287–289 (1977)
Herrera, L.J., Pomares, H., Rojas, I., Gonález, J., Valenzuela, O.: Function Approximation through Fuzzy Systems Using Taylor Series Expansion-Based Rules: Interpretability and Parameter Tuning. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 252–261. Springer, Heidelberg (2004)
Herrera, L.J., Pomares, H., Rojas, I., Valenzuela, O., Prieto, A.: TaSe, a Taylor Series Based Fuzzy System Model that Combines Interpretability and Accuracy. Fuzzy Sets and Systems (accepted)
Pomares, H., Rojas, I., Ortega, J., Prieto, A.: A systematic approach to a self-generating fuzzy rule-table for function approximation. IEEE Trans. Syst., Man, Cybern. 30, 431–447 (2000)
Bikdash, M.: A Highly Interpretable Form of Sugeno Inference Systems. IEEE Trans. Fuz. Syst. 7(6), 686–696 (1999)
Guillaume, S.: Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans. Fuzzy Systems 9, 426–443 (2001)
Johansen, T.A., Babuska, R.: Multiobjective Identification of Takagi-Sugeno Fuzzy Models. IEEE Trans. Fuz. Syst. 11(6), 847–860 (2003)
Rojas, I., Pomares, H., Bernier, J.L., Ortega, J., Pino, B., Pelayo, F.J., Prieto, A.: Time Series analysis using normalized PG-RBF network with regression weights. NeuroComputing 42, 267–285 (2002)
Decoste, D., Schoelkopf, B.: Training Invariant Support Vector Machines. Machine Learning 46, 161–190 (2002)
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Herrera, L.J., Pomares, H., Rojas, I., Guillén, A., Valenzuela, O., Prieto, A. (2005). TaSe Model for Long Term Time Series Forecasting. 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_126
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DOI: https://doi.org/10.1007/11494669_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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