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dc.contributor.authorLee, Ming-Chang
dc.date.accessioned2021-03-05T12:33:09Z
dc.date.available2021-03-05T12:33:09Z
dc.date.created2021-01-20T15:25:04Z
dc.date.issued2020
dc.identifier.isbn978-3-030-44041-1
dc.identifier.urihttps://hdl.handle.net/11250/2731871
dc.description.abstractDuring the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historical data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofProceedings of the 34th International Conference on Advanced Information Networking and Applications (AINA 2020)
dc.titleRePAD: Real-time Proactive Anomaly Detection for Time Seriesen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber1291-1302en_US
dc.identifier.doi10.1007/978-3-030-44041-1_110
dc.identifier.cristin1875738
dc.description.localcode"This is a post-peer-review, pre-copyedit version of an article. Locked until 28.3.2021 due to copyright restrictions. The final authenticated version is available online at: DOI "en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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