outlier_detection, detects outliers.
Project description
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
odds-0.1.3.tar.gz
(5.6 kB
view hashes)
Built Distribution
odds-0.1.3-py3-none-any.whl
(2.7 kB
view hashes)