pyacs.gts.lib.outliers package¶
Submodules¶
pyacs.gts.lib.outliers.find_l1trend module¶
- pyacs.gts.lib.outliers.find_l1trend.find_l1trend(self, lam, threshold, period=None, gap=10, components='NE', plot=False, verbose=False, in_place=False)[source]¶
- Parameters
self – Gts instance
lam – lambda parameter for L1 trend filtering
threshold – All residuals with threshold * standard deviation will be flagged as outliers
period – period(s) for searching outliers. Could be a single of a list of periods.
gap – number of days to consider that there is a gap. Default is gap=10.
components – components used for outliers detection
plot – boolean. If True, will plot the filter result and the flagged outliers
verbose – boolean. Verbose mode.
in_place – boolean. if True, apply to the original Gts. Default is False, returning a new Gts
- Returns
a new Gts instance if in_place is False or the current Gts
pyacs.gts.lib.outliers.find_outliers_around_date module¶
- pyacs.gts.lib.outliers.find_outliers_around_date.find_outliers_around_date(self, date, conf_level=95, n=3, lcomponent='NE', verbose=True)[source]¶
Find an outlier around a given date returns the index of the outlier, returns [] if no outlier found :param date : given date :param conf_level : confidence level for F_ratio test of outlier significance (default 95%%) :param n : number of dates either sides of date (default n=3) :param lcomponent : components ‘N’,’E’,’U’,’NE’,’NEU’ (default ‘NE’)
pyacs.gts.lib.outliers.find_outliers_percentage module¶
- pyacs.gts.lib.outliers.find_outliers_percentage.find_outliers_percentage(self, percentage=0.03, in_place=False, verbose=False, component='NEU', periods=None, excluded_periods=None)[source]¶
detrend a time series and ranks the residuals by increasing absolute value populate the outliers with the x % largest ones on each component