Abstract
This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary 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 calculates 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 nonstationarity 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 online supplementary materials.
| Original language | English |
|---|---|
| Number of pages | 15 |
| Journal | Journal of Computational and Graphical Statistics |
| DOIs | |
| Publication status | Accepted/In press - 2019 |
Keywords
- Concept drift
- Extreme value theory
- Feature-based time series analysis
- Kernel-based density estimation
- Multivariate time series
- Outlier detection
Cite this
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Anomaly detection in streaming nonstationary temporal data. / Talagala, Priyanga Dilini; Hyndman, Rob J.; Smith-Miles, Kate; Kandanaarachchi, Sevvandi; Muñoz, Mario A.
In: Journal of Computational and Graphical Statistics, 2019.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Anomaly detection in streaming nonstationary temporal data
AU - Talagala, Priyanga Dilini
AU - Hyndman, Rob J.
AU - Smith-Miles, Kate
AU - Kandanaarachchi, Sevvandi
AU - Muñoz, Mario A.
PY - 2019
Y1 - 2019
N2 - This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary 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 calculates 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 nonstationarity 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 online supplementary materials.
AB - This article proposes a framework that provides early detection of anomalous series within a large collection of nonstationary 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 calculates 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 nonstationarity 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 online supplementary materials.
KW - Concept drift
KW - Extreme value theory
KW - Feature-based time series analysis
KW - Kernel-based density estimation
KW - Multivariate time series
KW - Outlier detection
UR - http://www.scopus.com/inward/record.url?scp=85068173207&partnerID=8YFLogxK
U2 - 10.1080/10618600.2019.1617160
DO - 10.1080/10618600.2019.1617160
M3 - Article
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
SN - 1061-8600
ER -