Rare time series motif discovery from unbounded streams

Begum, Nurjahan; Keogh, Eamonn

doi:10.14778/2735471.2735476

Cited by 53 publications

(35 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Synthetic datasets are constructed based on UCR Archive [7]. UCR Archive is a popular time series repository, which includes a set of datasets widely used in time series mining researches [2,8,10]. To simulate patterns with various lengths, we select four datasets, Strawberry (Straw for short), Meat, NonInvasiveFa-talECG Thorax1 (ECG for short) and MALLAT whose time series lengths are 235, 448, 750 and 1024.…”

Section: Methodsmentioning

confidence: 99%

Matching Consecutive Subpatterns over Streaming Time Series

Kang

Wang

et al. 2018

Web and Big Data

View full text Add to dashboard Cite

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing different physical meanings in the real world. Hence, we formulate a new problem, called "consecutive subpatterns matching", which allows users to specify a pattern containing several consecutive subpatterns with various specified thresholds. We propose a novel representation Equal-Length Block (ELB) together with two efficient implementations, which work very well under all Lp-Norms without false dismissals. Extensive experiments are performed on synthetic and real-world datasets to illustrate that our approach outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation by orders of magnitude.

show abstract

Section: Methodsmentioning

confidence: 99%

Matching Consecutive Subpatterns over Streaming Time Series

Kang

Wang

et al. 2018

Web and Big Data

View full text Add to dashboard Cite

show abstract

“…[79] proposed aligned cluster analysis that extended spectral clustering to cluster time series, and applied the technique to discover facial events in unsupervised manner. On the other hand, time series motifs , defined as the closest pair of subsequences in one time series stream, can be discovered with a tractable exact algorithm [48], or an approximated algorithm that is capable of tackling never-ending streams [6]. Some attempts at measuring interactional synchrony include using face tracking and expressions [76], and rater-coding and pixel changes between adjacent frames [62].…”

Section: Related Workmentioning

confidence: 99%

A Branch-and-Bound Framework for Unsupervised Common Event Discovery

et al. 2017

View full text Add to dashboard Cite

Event discovery aims to discover a temporal segment of interest, such as human behavior, actions or activities. Most approaches to event discovery within or between time series use supervised learning. This becomes problematic when some relevant event labels are unknown, are difficult to detect, or not all possible combinations of events have been anticipated. To overcome these problems, this paper explores Common Event Discovery (CED), a new problem that aims to discover common events of variable-length segments in an unsupervised manner. A potential solution to CED is searching over all possible pairs of segments, which would incur a prohibitive quartic cost. In this paper, we propose an efficient branch-and-bound (B&B) framework that avoids exhaustive search while guaranteeing a globally optimal solution. To this end, we derive novel bounding functions for various commonality measures and provide extensions to multiple commonality discovery and accelerated search. The B&B framework takes as input any multidimensional signal that can be quantified into histograms. A generalization of the framework can be readily applied to discover events at the same or different times (synchrony and event commonality, respectively). We consider extensions to video search and supervised event detection. The effectiveness of the B&B framework is evaluated in motion capture of deliberate behavior and in video of spontaneous facial behavior in diverse interpersonal contexts: interviews, small groups of young adults, and parent-infant face-to-face interaction.

show abstract

“…Such existing studies are not multipurpose and focus on specific use cases, such as electrodermal activity recognition [21], long term physical activity and sleep recognition [22], Parkinson diseases monitoring [23], eating habit tracking [24], indoor location estimation [25], and anomalous activity detection [26]. However, to our knowledge, these works do not provide a detailed discussion about the challenges of collecting the data for the experiments.…”

Section: Wrist-mounted Wearablesmentioning

confidence: 99%

“…Market deployment for smartphone data collection has recently gotten the attention of the community [8,26,27]. As a result, a new category of user experiment is emerging, which is based on market deployed data collection.…”

Section: Smartphone Data Collectionmentioning

confidence: 99%

Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

Rawassizadeh

Momeni

Dobbins

et al. 2015

JSAN

View full text Add to dashboard Cite

Abstract:The ubiquity and affordability of mobile and wearable devices has enabled us to continually and digitally record our daily life activities. Consequently, we are seeing the growth of data collection experiments in several scientific disciplines. Although these have yielded promising results, mobile and wearable data collection experiments are often restricted to a specific configuration that has been designed for a unique study goal. These approaches do not address all the real-world challenges of "continuous data collection" systems. As a result, there have been few discussions or reports about such issues that are faced when "implementing these platforms" in a practical situation. To address this, we have summarized our technical and user-centric findings from three lifelogging and Quantified Self data collection studies, which we have conducted in real-world settings, for both smartphones and smartwatches. In addition to (i) privacy and (ii) battery related issues; based on our findings we recommend further works to consider (iii) implementing multivariate OPEN ACCESS J. Sens. Actuator Netw. 2015, 4 316 reflection of the data; (iv) resolving the uncertainty and data loss; and (v) consider to minimize the manual intervention required by users. These findings have provided insights that can be used as a guideline for further Quantified Self or lifelogging studies.

show abstract

Rare time series motif discovery from unbounded streams

Cited by 53 publications

References 27 publications

Matching Consecutive Subpatterns over Streaming Time Series

Matching Consecutive Subpatterns over Streaming Time Series

A Branch-and-Bound Framework for Unsupervised Common Event Discovery

Lesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices

Contact Info

Product

Resources

About