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Pattern discovery in multi-dimensional time series using multi-resolution matching
Pattern discovery in multi-dimensional time series using multi-resolution matching
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机译:使用多分辨率匹配的多维时间序列中的模式发现
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摘要
A method discovers patterns in unknown multi-dimensional data. A time-series of the multi-dimensional data is generated and a point cross-distance matrix is constructed by self-correlating the time-series. All minimum cost paths in the point cross-distance matrix are located at multiple time resolutions. The minimum cost paths are then related to temporal sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data.
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