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Predictive modeling with high-dimensional data streams: an on-line variable selection approach
Abstract : In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for each factor. This is achieved by means of only one sparse singular value decomposition which can be efficiently updated on-line and in an adaptive fashion. Simulation results based on artificial data streams demonstrate that the algorithm is able to select important variables in dynamic settings where the correlation structure among the observed streams is governed by a few hidden components and the importance of each variable changes over time. We also report on an application of our algorithm to a multivariate version of the ”enhanced index tracking” problem using financial data streams. The application consists of performing on-line asset allocation with the objective of overperforming two benchmark indices simultaneously.
Littérature citée [15 références]
https://hal.inria.fr/inria-00369564
Contributeur : Ist Rennes <>
Soumis le : vendredi 20 mars 2009 - 12:27:44
Dernière modification le : jeudi 7 février 2019 - 14:24:09
Document(s) archivé(s) le : jeudi 10 juin 2010 - 17:40:18
Contributeur : Ist Rennes <>
Soumis le : vendredi 20 mars 2009 - 12:27:44
Dernière modification le : jeudi 7 février 2019 - 14:24:09
Document(s) archivé(s) le : jeudi 10 juin 2010 - 17:40:18
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