Abstract
In this paper, we propose a general approach for the application of competitive neural networks to nonstationary time series prediction. The underlying idea is to combine the simplicity of the standard least-squares (LS) parameter estimation technique with the information compression power of unsupervised learning methods. The proposed technique builds the regression matrix and the prediction vector required by the LS method through the weight vectors of the K first winning neurons (i.e. those most similar to the current input vector). Since only few neurons are used to build the predictor for each input vector, this approach develops local representations of a nonstationary time series suitable for prediction tasks. Three competitive algorithms (WTA, FSCL and SOM) are tested and their performances compared with the conventional approach, confirming the efficacy of the proposed method.
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Barreto, G.A., Mota, J.C.M., Souza, L.G.M., Frota, R.A. (2004). Nonstationary Time Series Prediction Using Local Models Based on Competitive Neural Networks. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_117
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DOI: https://doi.org/10.1007/978-3-540-24677-0_117
Publisher Name: Springer, Berlin, Heidelberg
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