one_class_svm

Outlier detector for time series data using One Class SVM base on the moving mean and variance, unless the variance is low in which case the standard deviation will be used in place of variance. The algorithm parameters to be concerned with are 'window' which defines the length of sliding window to use, nu which defines the percentage that can be considered as outliers e.g. 0.1 would be 10%. Do note that if the variance is low each spike or trough will probably be identified as an outlier.

See the docstrings - https://earthgecko-skyline.readthedocs.io/en/latest/skyline.custom_algorithms.html#module-custom_algorithms.one_class_svm

See the custom_algorithm source - https://github.com/earthgecko/skyline/blob/master/skyline/custom_algorithms/one_class_svm.py