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    An active adaptation strategy for streaming time series classification based on elastic similarity measures

    Identifiers
    URI: http://hdl.handle.net/11556/1365
    ISSN: 0941-0643
    DOI: 10.1007/s00521-022-07358-3
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    Author/s
    Oregi, Izaskun; Pérez, Aritz; Del Ser, Javier; Lozano, Jose A.
    Date
    2022-05-21
    Keywords
    Time series classification
    Streaming data
    Deep learning
    Dynamic time warping
    Abstract
    In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern ...
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    article

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