Shape-based retrieval of similar subsequences in time-series databases

This paper deals with the problem of shape-based retrieval in time-series databases. The shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence. In this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. We first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. For efficient processing of the shape-based retrieval, we also propose the indexing and query processing methods. To verify the superiority of our approach, we perform extensive experiments with the real-world S&P 500 stock data. The results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup over the sequential scan method.

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