Electronic Journal of Statistics

Matrix factorization for multivariate time series analysis

Pierre Alquier and Nicolas Marie

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Matrix factorization is a powerful data analysis tool. It has been used in multivariate time series analysis, leading to the decomposition of the series in a small set of latent factors. However, little is known on the statistical performances of matrix factorization for time series. In this paper, we extend the results known for matrix estimation in the i.i.d setting to time series. Moreover, we prove that when the series exhibit some additional structure like periodicity or smoothness, it is possible to improve on the classical rates of convergence.

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Electron. J. Statist., Volume 13, Number 2 (2019), 4346-4366.

Received: March 2019
First available in Project Euclid: 6 November 2019

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Primary: 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 62H25: Factor analysis and principal components; correspondence analysis 62H12: Estimation 62M10: Time series, auto-correlation, regression, etc. [See also 91B84] 62G08: Nonparametric regression 93E14: Data smoothing 60G35: Signal detection and filtering [See also 62M20, 93E10, 93E11, 94Axx] 60B20: Random matrices (probabilistic aspects; for algebraic aspects see 15B52)

Multivariate Time Series Analysis matrix Factorization random Matrices non-parametric Regression

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Alquier, Pierre; Marie, Nicolas. Matrix factorization for multivariate time series analysis. Electron. J. Statist. 13 (2019), no. 2, 4346--4366. doi:10.1214/19-EJS1630. https://projecteuclid.org/euclid.ejs/1573009449

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