Anomaly Detection Using Dynamic Time Warping

Diab, Diab M.; AsSadhan, Basil; Binsalleeh, Hamad; Lambotharan, Sangarapillai; Kyriakopoulos, K.; Ghafir, Ibrahim

doi:10.1109/cse/euc.2019.00045

Cited by 28 publications

(22 citation statements)

References 21 publications

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“…The application of ADTW in the many other types of task to which DTW is often applied remains a productive direction for future investigation. These include similarity search [11], regression [12], clustering [13], anomaly and outlier detection [14], motif discovery [15], forecasting [16], and subspace projection [17]. One issue that will need to be addressed in each of these domains is how best to tune the amercing penalty ω, especially if a task does not have objective criteria by which utility may be judged.…”

Section: Discussionmentioning

confidence: 99%

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Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Herrmann¹,

Webb²

2021

Preprint

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Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW) variants, are time series distances with a wide range of applications.They minimize the cost of non-linear alignments between series. CDTW and WDTW have been introduced because DTW is too permissive in its alignments.However, CDTW uses a crude step function, allowing unconstrained flexibility within the window, and none beyond it. WDTW's multiplicative weight is relative to the distances between aligned points along a warped path, rather than being a direct function of the amount of warping that is introduced. In this paper, we introduce Amerced Dynamic Time Warping (ADTW), a new, intuitive, DTW variant that penalizes the act of warping by a fixed additive cost. Like CDTW and WDTW, ADTW constrains the amount of warping.However, it avoids both abrupt discontinuities in the amount of warping allowed and the limitations of a multiplicative penalty. We formally introduce ADTW, prove some of its properties, and discuss its parameterization. We show on a simple example how it can be parameterized to achieve an intuitive outcome, and demonstrate its usefulness on a standard time series classification benchmark.We provide a demonstration application in C++ [1].

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Section: Discussionmentioning

confidence: 99%

“…DTW is a foundational technique for a wide range of time series data analysis tasks, including similarity search [11], regression [12], clustering [13], anomaly and outlier detection [14], motif discovery [15], forecasting [16], and subspace projection [17].…”

Section: Background and Related Workmentioning

confidence: 99%

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Herrmann¹,

Webb²

2021

Preprint

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“…None of these papers apply DTW with the goal to improve anomaly detection. Another work by Diab et al uses DTW for anomaly detection [20]. Instead of using electrical measurements, it used network traffic data to detect network anomalies.…”

Section: Related Workmentioning

confidence: 99%

Detecting Anomalies using Overlapping Electrical Measurements in Smart Power Grids

Sontowski¹,

Lawrence²,

Deka³

et al. 2022

Preprint

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“…Anomaly detection seeks outliers or suspicious observations which differ significantly from the majority of the data. The difference between anomalous and non-anomalous data can be quantified by a variety of metrics such as Euclidean distance [14], mean squared error [9], correlation [15], cosine similarity [16], or dynamic time warping [17] between two observations, or the probability that an observation is drawn from a certain distribution [5,6] or it falls in a domain derived from that distribution [18]. Although there are numerous methods to tackle the problem from distinct angles, all methods can be decomposed to two steps: (1) derive a new sequence from the original MTS using a transformation or a predictive model; (2) calculate the "difference" metric for each element in the new sequence.…”

Section: Introductionmentioning

confidence: 99%

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

Liu¹,

Onnela²

2022

Preprint

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To detect aberrant human behaviors from large volume of passive data collected by smartphones in real time, we propose an online anomaly detection method using Hotelling’s T-squared test. The test statistic is a weighted average, with more weight on the between-individual component when there are little data available for the individual and more weight on the within-individual component when the data are adequate. The algorithm takes only O(1) run time in each update and the required memory usage is fixed after a pre-specified number of updates. The performance of the proposed method, in terms of accuracy, sensitivity and specificity, is consistently better than or equal to the offline method that it builds upon depending on the sample size of the individual data.

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Anomaly Detection Using Dynamic Time Warping

Cited by 28 publications

References 21 publications

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Amercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping

Detecting Anomalies using Overlapping Electrical Measurements in Smart Power Grids

Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data

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