Anomaly detection in streaming nonstationary temporal data

Priyanga Talagala, Rob Hyndman, Kate Smith-Miles (), Sevvandi Kandanaarachchi () and Mario Munoz ()

No 4/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: 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.

Keywords: concept drift; extreme value theory; feature-based time series analysis; kernel-based density estimation; multivariate time series; outlier detection. (search for similar items in EconPapers)
JEL-codes: C38 C55 C60 (search for similar items in EconPapers)
Pages: 23
Date: 2018
New Economics Papers: this item is included in nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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