Abstract
One of the basic problems arising under processing temporal dependencies is the analysis of time series. The various approaches to processing of temporal data are considered. The problem of anomaly detection among sets of time series is setting up. The algorithm TS-ADEEP-Multi for anomaly detection in time series sets for the case when the learning set contains examples of several classes is proposed. The method for improving the accuracy of anomaly detection, due to “compression” of these time series is used. Modelling results for anomaly detection in time series are produced.
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Fomina, M., Antipov, S., Vagin, V. (2016). Methods and Algorithms of Anomaly Searching in Collections of Time Series. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). Advances in Intelligent Systems and Computing, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-319-33609-1_6
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DOI: https://doi.org/10.1007/978-3-319-33609-1_6
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