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Novelets: a new primitive that allows online detection of emerging behaviors in time series

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Abstract

Much of the world’s data are 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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Notes

  1. The Roman satirist Juvenal wrote in AD 82 of rara avis in terris nigroque simillima cygno (“a rare bird in the lands, and very like a black swan”), meaning that since a black swan did not exist, the proposed “rare bird” did not exist. Here, “rare bird” was not literally a bird; it is just something that did not exist, like an honest politician.

  2. This story is reminiscent of, but is distinct from, the famous story of the discovery of the first computer “bug” (a moth) by Dr. Grace Hopper in 1945.

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Funding

We acknowledge funding from NSF award IIS 2103976.

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Authors and Affiliations

  1. University of California, Riverside, USA

    Ryan Mercer & Eamonn Keogh

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  1. Ryan Mercer
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  2. Eamonn Keogh
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R.M. wrote the main manuscript text and prepared figures. All authors reviewed the manuscript.

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Correspondence to Ryan Mercer.

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Mercer, R., Keogh, E. Novelets: a new primitive that allows online detection of emerging behaviors in time series. Knowl Inf Syst 66, 59–87 (2024). https://doi.org/10.1007/s10115-023-01936-0

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