Anomaly detection in streaming nonstationary temporal data
| Autor: | Priyanga Dilini Talagala, Mario A. Muñoz, Sevvandi Kandanaarachchi, Rob J. Hyndman, Kate Smith-Miles |
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| Rok vydání: | 2022 |
| Předmět: |
Statistics and Probability
Series (mathematics) Concept drift business.industry Anomaly (natural sciences) Pattern recognition 02 engineering and technology 01 natural sciences Novelty detection Temporal database 010104 statistics & probability Econometric and statistical methods 0202 electrical engineering electronic engineering information engineering Discrete Mathematics and Combinatorics 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence 0101 mathematics Statistics Probability and Uncertainty Econometrics not elsewhere classified business Extreme value theory Wireless sensor network |
| DOI: | 10.26180/21522408.v1 |
| Popis: | This article proposes a framework that provides early detection of anomalous series within a large collection of non-stationary streaming time series data. We define an anomaly as an observation that is very unlikely given the recent distribution of a given system. The proposed framework first forecasts a boundary for the system's typical behavior using extreme value theory. Then a sliding window is used to test for anomalous series within a newly arrived collection of series. The model uses time series features as inputs, and a density-based comparison to detect any significant changes in the distribution of the features. Using various synthetic and real world datasets, we demonstrate the wide applicability and usefulness of our proposed framework. We show that the proposed algorithm can work well in the presence of noisy non-stationarity data within multiple classes of time series. This framework is implemented in the open source R package oddstream. R code and data are available in the supplementary materials. |
| Databáze: | OpenAIRE |
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