{ "_id": "6ab119293d719c1f84087082", "Package": "IncDTW", "Type": "Package", "Title": "Incremental Calculation of Dynamic Time Warping", "Version": "1.1.4.6", "Authors@R": "person(given = \"Maximilian\", family = \"Leodolter\", role = c(\"aut\", \"cre\"), email = \"maximilian.leodolter@gmail.com\")", "Description": "The Dynamic Time Warping (DTW) distance measure for time\nseries allows non-linear alignments of time series to match\nsimilar patterns in time series of different lengths and or\ndifferent speeds. IncDTW is characterized by (1) the\nincremental calculation of DTW (reduces runtime complexity to a\nlinear level for updating the DTW distance) - especially for\nlife data streams or subsequence matching, (2) the vector based\nimplementation of DTW which is faster because no matrices are\nallocated (reduces the space complexity from a quadratic to a\nlinear level in the number of observations) - for all runtime\nintensive DTW computations, (3) the subsequence matching\nalgorithm runDTW, that efficiently finds the k-NN to a query\npattern in a long time series, and (4) C++ in the heart. For\ndetails about DTW see the original paper \"Dynamic programming\nalgorithm optimization for spoken word recognition\" by Sakoe\nand Chiba (1978) . For details\nabout this package, Dynamic Time Warping and Incremental\nDynamic Time Warping please see \"IncDTW: An R Package for\nIncremental Calculation of Dynamic Time Warping\" by Leodolter\net al. 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