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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.
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Submitted on : Friday, March 20, 2009 - 12:27:44 PM
Last modification on : Tuesday, January 18, 2022 - 2:26:04 PM
Long-term archiving on: : Thursday, June 10, 2010 - 5:40:18 PM


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  • HAL Id : inria-00369564, version 1



Brian Mcwilliams, Giovanni Montana. Predictive modeling with high-dimensional data streams: an on-line variable selection approach. SPARS'09 - Signal Processing with Adaptive Sparse Structured Representations, Inria Rennes - Bretagne Atlantique, Apr 2009, Saint Malo, France. ⟨inria-00369564⟩



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