Outlier Data Detection Systems - ODDS

As used in paper "Simple Models are Effective in Anomaly Detection in Multi-variate Time Series"

Contains an OD object

Instantiate the object with the 'algo' argument

eg. od = OD('VAR') instantiates an outlier detection system using vector autoregression

get outlier scores using the 'get_os()' function

eg. outlier_scores = od.get_os(X)

Where X is a data matrix, n samples by p features. P must be 2 or greater to work on many of the systems, this returns a vector with n scores, one for each sample.

Higher numbers mean more outlying.

Valid strings for outlier algorithms:

• 'VAR' vector autoregression
• 'FRO' ordinary feature regression
• 'FRL' LASSO feature regression
• 'FRR' Ridge feature regression
• 'GMM' Gaussian Mixture model
• 'IF' isolation Forest
• 'DBSCAN' Density Based Spatial clustering and noise
• 'OCSVM' one class support vector machine
• 'LSTM' long short term memory
• 'GRU' gated recurrent unit
• 'AE' autoencoder
• 'VAE' variational autoencoder
• 'OP' outlier pursuit
• 'GOP' graph regularised outlier pursuit
• 'RAND' random scoring (for baseline comparison)