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Scalable structural break detection

Tamas Elteto 1 Nikolaus Hansen 1 Cecile Germain-Renaud 1, 2 Pascal Bondon 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This paper deals with a statistical model fitting procedure for non-stationary time series. This procedure selects the parameters of a piecewise autoregressive model using the Minimum Description Length principle. The existing chromosome representation of the piecewise autoregressive model and its corresponding optimisation algorithm are improved. First, we show that our proposed chromosome representation better captures the intrinsic properties of the piecewise autoregressive model. Second, we apply an optimisation algorithm, the Covariance Matrix Adaptation - Evolution Strategy, with which our setup converges faster to the optimal fit. Our proposed method achieves at least one order of magnitude performance improvement compared to the existing solution.
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Submitted on : Monday, June 25, 2012 - 8:38:19 PM
Last modification on : Tuesday, July 20, 2021 - 3:05:14 AM
Long-term archiving on: : Wednesday, September 26, 2012 - 3:05:45 AM


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Tamas Elteto, Nikolaus Hansen, Cecile Germain-Renaud, Pascal Bondon. Scalable structural break detection. Applied Soft Computing, Elsevier, 2012, ⟨10.1016/j.asoc.2012.06.002⟩. ⟨hal-00711843⟩



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