Abstract
It is well known fact that organizing different predictors in an ensemble increases the accuracy of prediction of the time series. This paper discusses different methods of integration of predictors cooperating in an ensemble. The considered methods include the ordinary averaging, weighted averaging, application of principal component analysis to the data, blind source separation as well as application of additional neural predictor as an integrator. The proposed methods will be verified on the example of prediction of 24-hour ahead load pattern in the power system, as well as prediction of the environmental pollution for the next day.
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Osowski, S., Siwek, K. (2011). Methods of Integration of Ensemble of Neural Predictors of Time Series - Comparative Analysis. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_5
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DOI: https://doi.org/10.1007/978-3-642-20282-7_5
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