Exact Indexing of Dynamic Time Warping

Publisher Summary The indexing of very large time series databases has attracted the attention of database community in recent years. The vast majority of work in this area has focused on indexing under the Euclidean distance metric. The problem of indexing time series has attracted much research interest in the database community. Most algorithms that are used to index time series utilize the Euclidean distance or some variation thereof. However, it has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic time warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis. Because of this flexibility, DTW is widely used in science, medicine, industry, and finance. Unfortunately, however, DTW does not obey the triangular inequality and, thus, has resisted attempts at exact indexing. Instead, many researchers have introduced approximate indexing techniques, or abandoned the idea of indexing and concentrated on speeding up sequential search.

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