Sparse partial least squares for on-line variable selection in multivariate data streams

TL;DR

This paper introduces an efficient online algorithm for variable selection in high-dimensional multivariate data streams, leveraging sparse partial least squares to adaptively identify important variables and latent factors in dynamic environments.

Contribution

The paper presents a novel online sparse PLS algorithm that updates variable importance and latent factors efficiently using a single sparse SVD, suitable for high-dimensional streaming data.

Findings

Successfully selects important variables in dynamic settings

Effectively tracks assets in financial data streams

Adapts to changing correlation structures over time

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.