Abstract
The analysis of different time series is an important activity in many areas of science and engineering. In this paper, we introduce a new method (feature extraction for time series) and an application (TimeExplorer) for similarity-based time series querying. The method is based on eleven characterizations of line graphs presenting time series. These characterizations include measures, such as, means, standard deviations, differences, and periodicities. A similarity metric is then computed on these measures. Finally, we use the similarity metric to search for similar time series in the database.
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Dang, T.N., Wilkinson, L. (2013). TimeExplorer: Similarity Search Time Series by Their Signatures. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_28
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DOI: https://doi.org/10.1007/978-3-642-41914-0_28
Publisher Name: Springer, Berlin, Heidelberg
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