Kernel Based Online Change Point Detection | IEEE Conference Publication | IEEE Xplore

Kernel Based Online Change Point Detection


Abstract:

Detecting change points in time series data is a challenging problem, in particular when no prior information on the data distribution and the nature of the change is ava...Show More

Abstract:

Detecting change points in time series data is a challenging problem, in particular when no prior information on the data distribution and the nature of the change is available. In a former work, we introduced an online non-parametric change-point detection framework built upon direct density ratio estimation over two consecutive time segments, rather than modeling densities separately. This algorithm based on the theory of reproducing kernels showed positive and reliable detection results for a variety of problems. To further improve the detection performance of this approach, we propose in this paper to modify the original cost function in order to achieve unbiasedness of the density ratio estimation under the null hypothesis. Theoretical analysis and numerical simulations confirm the improved behavior of this method, as well as its efficiency compared to a state of the art one. Application to sentiment change detection in Twitter data streams is also presented.
Date of Conference: 02-06 September 2019
Date Added to IEEE Xplore: 18 November 2019
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Conference Location: A Coruna, Spain

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