Extreme event timing
The DN_OutlierInclude features measure the timing of extreme events relative to the start and end of the time series.
catch22 contains two features based on the DN_OutlierInclude function in hctsa:
outlier_timing_pos(the hctsa featureDN_OutlierInclude_p_001_mdrmd) i.e, themdrmdoutput from runningDN_OutlierInclude(x_z,'pos',0.01)in hctsa.outlier_timing_neg(the hctsa featureDN_OutlierInclude_n_001_mdrmd)i.e., themdrmdoutput from runningDN_OutlierInclude(x_z,'neg',0.01)in hctsa).
What these features do
These features involve the following steps:
z-score the input time series.
Initialise an equally spaced set of increments, from zero to the maximum values of the time series, in the case of
outlier_timing_pos(or from 0 to the minimum value of the time series in the case ofoutlier_timing_neg). In this way, a set of increasingly `extreme' deviations from the mean (either deviations above-the-mean or below-the-mean) are analysed across the loop in Step (3).At each threshold set in Step (2):
Determine the time points in which the time series is `over-threshold'.
Compute the median index of all such over-threshold time points, as
rmd.For interpretation, and to appropriately compare time series of different lengths, we then linearly re-scale
rmdsuch that a median right in the middle of the time series, at indexN/2, maps to 0, a value at the end of the time series, at indexN, maps to 1, and a value at the start of the time series, index1, maps to a -1.
The final statistic returns the median of all values of
rmdvalues across all values of the threshold, as the output statistic.
What it measures
These statistics measure whether over-threshold events (either positive or negative deviations from the mean) tend to be positioned relative near the start of the time series (output values near -1), approximately equally likely to be anywhere through the time series (output values near 0), or more likely to be near the end of the time series (output values near 1). These features thus capture something related to the stationarity of over-threshold events.
To give an intuition, below we plot some examples of how rmd at a fixed threshold (80% the maximum positive deviation) for the case of outlier_timing_pos ( note that the full statistic takes the median of rmd across a range of thresholds, as described above).
Consider these examples:
Time series, that have extreme events (red dots, relative to the threshold, shown as a dashed red line) distributed similarly across time, will yield values close to zero for this statistic (vertical blue line). For example these:
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