Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash
Pages 19 - 32
Abstract
Dynamic Time Warping (DTW) is a well-known similarity measure between time series data. Although DTW can calculate the similarity between time series with different lengths, it is computationally expensive. Therefore, fast algorithms that approximate the DTW have been desired. SSH (Sketch, Shingle & Hash) is a representative hash-based approximation algorithm. It extracts a set of quantized subsequences from a given time series and finds similar time series by means of Min-Hash, a hash-based set similarity search. However, Min-Hash does not care about the location of set elements (i.e., quantized subsequences) in the time series, so that hash collisions have a rather weak correlation with DTW. In this paper, to strengthen the correlation between hash collisions and DTW, we propose a new method termed Section Min-Hash that also allows position shifts required by DTW. After quantizing subsequences in a time series based on Euclidean distance, Section Min-Hash explicitly specifies multiple sections within the time series and generates the hash values from all the sections.
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Oct 2023
324 pages
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 27 October 2023
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