Time series motifs discovery under DTW allows more robust discovery of conserved structure

Alaee, Sara; Mercer, Ryan; Kamgar, Kaveh; Keogh, Eamonn

doi:10.1007/s10618-021-00740-0

Cited by 34 publications

(20 citation statements)

References 30 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

View full text Add to dashboard Cite

show abstract

“…Time series motifs can also be used to create time series clusters using different approaches to similarity measures. A novel approach, which uses dynamic time warping (DTW) to measure the similarity between time series, was recently proven to have significant performance benefits over other methods, as presented in [37]. Dynamic time warping [38] is a well-known method that has a wide application on time series, such as finding patterns [39] and classification [40].…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

Hlupic¹,

Orescanin²,

Baranović

2022

IEEE Access

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Internet Protocol Television (IPTV) has had a significant impact on live TV content consumption in the past decade, as improvements in the broadband speed have allowed more data volume to be delivered. In addition to existing infrastructure, which is mostly based on the set top boxes, new content providers have emerged, utilizing newly developed proprietary streaming platforms. As the number of IPTV users grew, more volume and variety of data became available for analysis. By analyzing stored user actions, it is possible to create a multivariate time series that represents user behavior over time. The approach presented in the paper is based on multivariate time series generation from user data and determining the similarity between them. Time series are created for each user based on the proposed quantified action sets, grouped in the feature groups and summarized over time. The action sets and feature groups can be adjusted to a certain IPTV platform. The end result of the analysis is the similarity score matrix, generated by calculating the similarities of all users' time series, where the similarity measure calculation can be chosen arbitrarily.INDEX TERMS IPTV, time series analysis, data analysis, user behavior analysis, time series similarity, user profiling.

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“…We give two variants [52] of the procedure which focus on cluster transitions and therefore are capable to detect a new sort of outliers, which are based on the behavior of time series in relation to its cluster peers. The implementation of the approaches as well as the generated data sets are available on Github 2 .…”

Section: Introductionmentioning

confidence: 99%

Cluster-based stability evaluation in time series data sets

2022

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In modern data analysis, time is often considered just another feature. Yet time has a special role that is regularly overlooked. Procedures are usually only designed for time-independent data and are therefore often unsuitable for the temporal aspect of the data. This is especially the case for clustering algorithms. Although there are a few evolutionary approaches for time-dependent data, the evaluation of these and therefore the selection is difficult for the user. In this paper, we present a general evaluation measure that examines clusterings with respect to their temporal stability and thus provides information about the achieved quality. For this purpose, we examine the temporal stability of time series with respect to their cluster neighbors, the temporal stability of clusters with respect to their composition, and finally conclude on the temporal stability of the entire clustering. We summarise these components in a parameter-free toolkit that we call Cluster Over-Time Stability Evaluation (CLOSE). In addition to that we present a fuzzy variant which we call FCSETS (Fuzzy Clustering Stability Evaluation of Time Series). These toolkits enable a number of advanced applications. One of these is parameter selection for any type of clustering algorithm. We demonstrate parameter selection as an example and evaluate results of classical clustering algorithms against a well-known evolutionary clustering algorithm. We then introduce a method for outlier detection in time series data based on CLOSE. We demonstrate the practicality of our approaches on three real world data sets and one generated data set.

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Time series motifs discovery under DTW allows more robust discovery of conserved structure

Cited by 34 publications

References 30 publications

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

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

A Novel Method for IPTV Customer Behavior Analysis Using Time Series

Cluster-based stability evaluation in time series data sets

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