Detecting Changes in Time Series Data using Volatility Filters
For practitioners analyzing time series data, this work provides new tools for detecting volatility changes, but the improvements are incremental over existing methods.
This paper introduces change detection algorithms for transient changes in time series volatility using windowed volatility filters, with methods for both univariate and multivariate data. The proposed methods achieve effective detection and location estimation, validated on synthetic and real-world data.
This work develops techniques for the sequential detection and location estimation of transient changes in the volatility (standard deviation) of time series data. In particular, we introduce a class of change detection algorithms based on the windowed volatility filter. The first method detects changes by employing a convex combination of two such filters with differing window sizes, such that the adaptively updated convex weight parameter is then used as an indicator for the detection of instantaneous power changes. Moreover, the proposed adaptive filtering based method is readily extended to the multivariate case by using recent advances in distributed adaptive filters, thereby using cooperation between the data channels for more effective detection of change points. Furthermore, this work also develops a novel change point location estimator based on the differenced output of the volatility filter. Finally, the performance of the proposed methods were evaluated on both synthetic and real world data.