Euan McGonigle (University of Bristol)

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Title: Nonparametric Change Point Detection for Multivariate Time Series

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Abstract: In time series analysis, many data sets of practical interest contain abrupt changes in structure, such as the mean level or serial dependence. Nonparametric change point detection is a flexible approach which aims to find general distributional changes in the data. In this talk, we propose a methodology for nonparametric detection of multiple change points in multivariate time series. We define a notion of distributional change using a weighted integral of the joint characteristic function of the time series and its lagged values. This is used in combination with a moving sum-type procedure to identify multiple change points by finding local maximisers of a V-statistic calculated in a rolling fashion over the data. This enables the detection of changes in both the marginal and pairwise joint distributions of a serially dependent time series. We examine the theoretical properties of the procedure and illustrate the flexibility of the method by applying it to a data example from neuroscience.

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