morpheus.stages.postprocess.timeseries_stage

(Latest Version)

Functions

calc_bin(obj, time0, resolution_sec) Calculates the bin spacing between the start and stop timestamp at a specified resolution.
fftAD(signalvalues[, percentile, zthresh, ...]) Detect anomalies with fast fourier transform.
round_seconds(obj) Returns the given timestamp with rounded seconds.
to_periodogram(signal_cp) Returns periodogram of signal for finding frequencies that have high energy.
zscore(data) Calculate z score of cupy.ndarray.

Classes

TimeSeriesStage(c[, resolution, min_window, ...]) Perform time series anomaly detection and add prediction.
calc_bin(obj, time0, resolution_sec)[source]

Calculates the bin spacing between the start and stop timestamp at a specified resolution.

fftAD(signalvalues, percentile=90, zthresh=8, lowpass=None)[source]

Detect anomalies with fast fourier transform.

Parameters
signalvalues

Values of time signal (real valued).

percentile

Filtering percentile for spectral density based filtering, by default 90.

zthresh

Z-score threshold, can be tuned for datasets and sensitivity, by default 8.

lowpass

Filtering percentile for frequency based filtering, by default None.

Returns
cupy.ndarray

Binary vector whether each point is anomalous.

round_seconds(obj)[source]

Returns the given timestamp with rounded seconds.

Parameters
obj

Timestamp obj.

Returns
pd.Timestamp

Timestamp with rounded seconds.

to_periodogram(signal_cp)[source]

Returns periodogram of signal for finding frequencies that have high energy.

Parameters
signal_cp

Signal (time domain).

Returns
cupy.ndarray

CuPy array representing periodogram.

zscore(data)[source]

Calculate z score of cupy.ndarray.

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