A unifying framework for detecting outliers and change points from time series

By Jun'ichi Takeuchi, Kenji Yamanishi. IEEE Transactions on Knowledge and Data Engineering, 2006

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Deep learning in video multi-object tracking: A survey (2019, methodology): “Finally, a changing point detection [161] algorithm was employed to detect abrupt changes in stationary time series representing track segments; this was done in order to detect track drift, to remove unstable track segments and to combine the segments together.”
Classifier Ensembles for Detecting Concept Change in Streaming Data: Overview and Perspectives (2008, background): “Detecting changes in multi-dimensional streaming data has been gaining speed in recent years [25, 36].”
Change-Point Detection in Time-Series Data by Relative Density-Ratio Estimation (2012, methodology): “…datasets that contain manually inserted change-points: • Dataset 1 (Jumping mean): The following 1-dimensional auto-regressive model borrowed from Takeuchi and Yamanishi (2006) is used to generate 5000 samples (i.e., t = 1 , . . . , 5000): where ǫ t is a Gaussian noise with mean µ and…”