morpheus.stages.postprocess.timeseries_stage

Functions

 calc_bin(obj, t0, resolution_sec) Calculates the bin spacing between the start and stop timestamp at a specified resolution. fftAD(signalvalues[, p, zt, lowpass]) 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, t0, resolution_sec)[source]

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

fftAD(signalvalues, p=90, zt=8, lowpass=None)[source]

Detect anomalies with fast fourier transform.

Parameters
signalvaluescupy.ndarray

Values of time signal (real valued).

pint, optional

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

ztint, optional

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

lowpass_type_, optional

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
objpd.Timestamp

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_cpcupy.ndarray

Signal (time domain).

Returns
cupy.ndarray

CuPy array representing periodogram.

zscore(data)[source]

Calculate z score of cupy.ndarray.

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