Online Amnestic DTW to allow Real-Time Golden Batch Monitoring

Yeh, Chin‐Chia Michael; Zhu, Yada; Dau, Son Hoang; Darvishzadeh, Amirali; Noskov, Mikhail; Keogh, Eamonn

doi:10.1145/3292500.3330650

Cited by 15 publications

(4 citation statements)

References 17 publications

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“…The dynamic time warping (DTW) method is one of the most popular algorithms used to simplify the representation of time series data. Many variations of this algorithm were developed (e.g., local DTW from Yeh et al 2019) since Berndt and Clifford (1994) suggested the use of DTW to identify patterns in time series data. The underlying process systematically compares data points between two vectors to find the distance between two time series data sets.…”

Section: Related Workmentioning

confidence: 99%

Visual analysis of blow molding machine multivariate time series data

Musleh

Chatzimparmpas

Jusufi

2022

J Vis

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The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners’ requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results. Graphical Abstract

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Section: Related Workmentioning

confidence: 99%

Visual analysis of blow molding machine multivariate time series data

Musleh

Chatzimparmpas

Jusufi

2022

J Vis

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“…However, an emerging behavior is suggestive of an undocumented and unexpected change. For example, an operator may be unconsciously compensating for degrading infeed product by allowing a pasteurizing regime to run longer than normal [35]. The plant manager would like to be made aware of such emerging patterns.…”

Section: Icu Respiration Datamentioning

confidence: 99%

Novelets: A New Primitive that Allows Online Detection of Emerging Behaviors in Time Series

Mercer

Keogh

2022

Preprint

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Much of the world’s data is time series. While offline exploration of time series can be useful, time series is almost unique in allowing the possibility of direct and immediate intervention. For example, if we are monitoring an industrial process and have an algorithm that predicts imminent failure, we could direct a controller to open a pressure release valve or initiate an evacuation plan. There is a plethora of tools to monitor time series for known behaviors (pattern matching), previously unknown highly conserved behaviors (motifs), evolving behaviors (chains) and unexpected behaviors (anomalies). In this work, we claim that there is another useful primitive, emerging behaviors, that are worth monitoring for. We call such behaviors Novelets. We explain that Novelets are not anomalies, chains, or motifs but can be informally thought of as initially apparent anomalies that are later discovered to be motifs. We will show that Novelets have a natural interpretation in many disciplines, including science, medicine, and industry. As we will further demonstrate, Novelet discovery can have many downstream uses, including prognostics and abnormal behavior detection. We will demonstrate the utility of our proposed primitive on a diverse set of challenging domains.

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“…• Monitoring of batch processes is a slight generalization of the above. At every time point in a single run (plus or minus some "wiggle room" that can be modeled [25]), we know what range of values are acceptable. If the reading begins to drift outside that range, we can sound an alarm.…”

Section: Appendix a On The Term Early Classificationmentioning

confidence: 99%

When is Early Classification of Time Series Meaningful

Wu¹,

Der²,

Keogh³

2021

IEEE Trans. Knowl. Data Eng.

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Since its introduction two decades ago, there has been increasing interest in the problem of early classification of time series. This problem generalizes classic time series classification to ask if we can classify a time series subsequence with sufficient accuracy and confidence after seeing only some prefix of a target pattern. The idea is that the earlier classification would allow us to take immediate action, in a domain in which some practical interventions are possible. For example, that intervention might be sounding an alarm or applying the brakes in an automobile. In this work, we make a surprising claim. In spite of the fact that there are dozens of papers on early classification of time series, it is not clear that any of them could ever work in a real-world setting. The problem is not with the algorithms per se but with the vague and underspecified problem description. Essentially all algorithms make implicit and unwarranted assumptions about the problem that will ensure that they will be plagued by false positives and false negatives even if their results suggested that they could obtain near-perfect results. We will explain our findings with novel insights and experiments and offer recommendations to the community.

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Online Amnestic DTW to allow Real-Time Golden Batch Monitoring

Cited by 15 publications

References 17 publications

Visual analysis of blow molding machine multivariate time series data

Visual analysis of blow molding machine multivariate time series data

Novelets: A New Primitive that Allows Online Detection of Emerging Behaviors in Time Series

When is Early Classification of Time Series Meaningful

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