Abstract
In this paper we develop ensemble method based on multivariate decompositions taken from blind signal separation techniques. The main idea is to decompose prediction result into constructive and destructive (noises) components. Elimination of the noises from predictions should improve final prediction. One of the key issues in this method is the correct classification and distinction between destructive and constructive components, what provide to random noise detection problem. It can be interpreted in terms of signal similarity, in which the Bose-Einstein divergence can be applied.
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Szupiluk, R. (2013). Noise Identification in Multivariate Time Series Modelling with Divergence Approach. In: Kobyliński, A., Sobczak, A. (eds) Perspectives in Business Informatics Research. BIR 2013. Lecture Notes in Business Information Processing, vol 158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40823-6_26
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DOI: https://doi.org/10.1007/978-3-642-40823-6_26
Publisher Name: Springer, Berlin, Heidelberg
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