Time series produced by dynamical systems as frequently the case in neuroscience  are rarely stationary  but often exhibit quite abrupt changes due to bifurcations or other dynamical phenomena . A plethora of methods for detecting such changes in time series statistics , commonly called change point analysis, have been developed over the years, in addition to test criteria to evaluate change point significance. Issues to consider when developing such methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many change points as contained in potentially high-dimensional data sets . Here, a general method called Paired Adaptive Regressors for Cumulative Sum (PARCS) is developed for detecting multiple change points in  multivariate time series. The method's flexibility to incorporate useful features from other change point detection techniques is highlighted . The advantages of PARCS over existing approaches are demonstrated through a series of simulation experiments,  followed by a real data application to neural recordings from rat medial prefrontal cortex during learning.
Time series , as frequently the case in neuroscience , are rarely stationary , but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them . A plethora of methods for detecting such change points in time series statistics have been developed over the years, in addition to test criteria to evaluate their significance. Issues to consider when developing change point analysis methods include computational demands, difficulties arising from either limited amount of data or a large number of covariates, and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially high-dimensional time series . Here, a general method called Paired Adaptive Regressors for Cumulative Sum is developed for detecting multiple change points in the mean of multivariate time series. The method's flexibility to incorporate useful features from state-of-the-art change point detection techniques is highlighted , and its advantages over alternative approaches are demonstrated through a series of simulation experiments, along with potential drawbacks and suggestions to remedy them. This is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning.
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produced by dynamical systems
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,
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,
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quite
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bifurcations or other dynamical phenomena
attractor transitions or bifurcations in the dynamical systems producing them
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changes
change points
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, commonly called change point analysis,
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change point
their
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such
change point analysis
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change points
changes
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data sets
time series
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(PARCS)
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the mean of
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other
state-of-the-art
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. The advantages of PARCS over existing
, and its advantages over alternative
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along with potential drawbacks and suggestions to remedy them. This is