Abstract
The discovery of repeated subsequences, time series motifs, is a problem which has great utility for several higher-level data mining tasks, including classification, clustering, forecasting and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static time series data. However, for many applications, the streaming nature of time series demands a new kind of methods for discovery of time series motifs. In this paper, we develop a new method for motif discovery in streaming time series. In this method we use significant extreme points to determine motif candidates and then cluster motif candidates by BIRCH algorithm. The method is very effective not only for large time series data but also for streaming environment since it needs only one-pass of scan through the whole data.
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Truong, C.D., Anh, D.T. (2014). An Efficient Method for Discovering Motifs in Streaming Time Series Data. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 244. Springer, Cham. https://doi.org/10.1007/978-3-319-02741-8_20
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DOI: https://doi.org/10.1007/978-3-319-02741-8_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02740-1
Online ISBN: 978-3-319-02741-8
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