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Locally adaptive factor processes for multivariate time series

Publication ,  Journal Article
Durante, D; Scarpa, B; Dunson, DB
Published in: Journal of Machine Learning Research
January 1, 2014

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inferences and predictions, with over-smoothing across erratic time intervals and under-smoothing across times exhibiting slow variation. This can lead to mis-calibration of predictive intervals, which can be substantially too narrow or wide depending on the time. We propose a locally adaptive factor process for characterizing multivariate mean-covariance changes in continuous time, allowing locally varying smoothness in both the mean and covariance matrix. This process is constructed utilizing latent dictionary functions evolving in time through nested Gaussian processes and linearly related to the observed data with a sparse mapping. Using a diffential equation representation, we bypass usual computational bottlenecks in obtaining MCMC and online algorithms for approximate Bayesian inference. The performance is assessed in simulations and illustrated in a financial application. © 2014 Daniele Durante, Bruno Scarpa and David B. Dunson.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2014

Volume

15

Start / End Page

1493 / 1522

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

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Durante, D., Scarpa, B., & Dunson, D. B. (2014). Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research, 15, 1493–1522.
Durante, D., B. Scarpa, and D. B. Dunson. “Locally adaptive factor processes for multivariate time series.” Journal of Machine Learning Research 15 (January 1, 2014): 1493–1522.
Durante D, Scarpa B, Dunson DB. Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research. 2014 Jan 1;15:1493–522.
Durante, D., et al. “Locally adaptive factor processes for multivariate time series.” Journal of Machine Learning Research, vol. 15, Jan. 2014, pp. 1493–522.
Durante D, Scarpa B, Dunson DB. Locally adaptive factor processes for multivariate time series. Journal of Machine Learning Research. 2014 Jan 1;15:1493–1522.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

January 1, 2014

Volume

15

Start / End Page

1493 / 1522

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences