Scalable structural break detection

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 (CMA-ES), 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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► The context of the paper is the analysis of large volume of non-stationary data.
► We fit a piecewise AR model to the analysed time series using the MDL principle.
► The existing method AutoPARM scales inefficiently with the data volume.
► We propose an alternative optimisation strategy using CMA-ES for the fitting.
► Our method achieves at least one order of magnitude performance improvement.