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The Spectral Residual outlier detector is based on the paper Time-Series Anomaly Detection Service at Microsoft and is suitable for unsupervised online anomaly detection in univariate time series data. The algorithm first computes the Fourier Transform of the original data. Then it computes the spectral residual of the log amplitude of the transformed signal before applying the Inverse Fourier Transform to map the sequence back from the frequency to the time domain. This sequence is called the saliency map. The anomaly score is then computed as the relative difference between the saliency map values and their moving averages. If the score is above a threshold, the value at a specific timestep is flagged as an outlier. For more details, please check out the paper.
threshold: threshold used to classify outliers. Relative saliency map distance from the moving average.
window_amp: window used for the moving average in the spectral residual computation. The spectral residual is the difference between the log amplitude of the Fourier Transform and a convolution of the log amplitude over
window_local: window used for the moving average in the outlier score computation. The outlier score computes the relative difference between the saliency map and a moving average of the saliency map over
n_est_points: number of estimated points padded to the end of the sequence.
n_grad_points: number of points used for the gradient estimation of the additional points padded to the end of the sequence. The paper sets this value to 5.
Initialized outlier detector example:
from alibi_detect.od import SpectralResidual od = SpectralResidual( threshold=1., window_amp=20, window_local=20, n_est_points=10, n_grad_points=5 )
It is often hard to find a good threshold value. If we have a time series containing both normal and outlier data and we know approximately the percentage of normal data in the time series, we can infer a suitable threshold:
od.infer_threshold( X, t=t, # array with timesteps, assumes dt=1 between observations if omitted threshold_perc=95 )
We detect outliers by simply calling
predict on a time series
X to compute the outlier scores and flag the anomalies. We can also return the instance (timestep) level outlier score by setting
return_instance_score to True.
The prediction takes the form of a dictionary with
meta contains the detector’s metadata while
data is also a dictionary which contains the actual predictions stored in the following keys:
is_outlier: boolean whether instances are above the threshold and therefore outlier instances. The array is of shape (timesteps,).
instance_score: contains instance level scores if
preds = od.predict( X, t=t, # array with timesteps, assumes dt=1 between observations if omitted return_instance_score=True )