morpheus.stages.postprocess.timeseries_stage
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
- signalvaluescupy.ndarray
Values of time signal (real valued).
- percentileint, optional
Filtering percentile for spectral density based filtering, by default 90.
- zthreshint, 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.