oddstream: oddstream: A package for Outlier Detection in Data Streams

Description Note References See Also

Description

Rapid advances in hardware technology have enabled a wide range of physical objects, living beings and environments to be monitored using sensors attached to them. Over time these sensors generate streams of time series data. Finding anomalous events in streaming time series data has become an interesting research topic due to its wide range of possible applications such as: intrusion detection, water contamination monitoring, machine health monitoring, etc. This package proposes a framework that provides real time support for early detection of anomalous series within a large collection of streaming time series data. By definition, anomalies are rare in comparison to a system's typical behaviour. We define an anomaly as an observation that is very unlikely given the forecast distribution. The proposed framework first forecasts a boundary for the system's typical behaviour using a representative sample of the typical behaviour of the system. An approach based on extreme value theory is used for this boundary prediction process. Then a sliding window is used to test for anomalous series within the newly arrived collection of series. Feature based representation of time series is used as the input to the model. To cope with concept drift, the forecast boundary for the system's typical behaviour is updated periodically. More details regarding the algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019) DOI:10.1080/10618600.2019.1617160.

Note

The name oddstream comes from Outlier Detection in Data STREAMs

References

Clifton, D. A., Hugueny, S., & Tarassenko, L. (2011). Novelty detection with multivariate extreme value statistics. Journal of signal processing systems, 65 (3),371-389.

Talagala, P. D., Hyndman, R. J., Smith-Miles, K., et al. (2019). Anomaly detection in streaming nonstationary temporal data. Journal of Computational and Graphical Statistics, 1-28. DOI:10.1080/10618600.2019.1617160.

See Also

The core functions in this package: find_odd_streams, extract_tsfeatures, get_pc_space, set_outlier_threshold, gg_featurespace


pridiltal/oddstream documentation built on April 3, 2020, 11:48 a.m.