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Two-Sample Testing for Event Impacts in Time Series
[article]
2020
arXiv
pre-print
In many application domains, time series are monitored to detect extreme events like technical faults, natural disasters, or disease outbreaks. Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest. In this work, we thus propose a non-parametric
arXiv:2001.11930v1
fatcat:hohgdsm67fe77ix2plbioksbxq