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Many methods have been proposed to measure the similarity between time series data sets, each with advantages and weaknesses. It is to choose the most appropriate similarity measure depending on the intended application domain and data considered. The performance of machine learning algorithms depends on the metric used to compare two objects. For time series, Dynamic Time Warping (DTW) is the most...
The purpose of Dynamic Time Warping (DTW) is to determine the shortest warp path, corresponding to the optimal alignment between two sequences. It is one of the most used methods for time series distance measure. DTW was introduced to the community as a Data Mining utility for various tasks for time series problems such as classification and clustering. Many variants of DTW aim to accelerate the calculation...
Measuring similarity or distance between two data points is fundamental to many Machine Learning algorithms such as K-Nearest-Neighbor, Clustering etc. Depending on the nature of the data point, various measurements can be used. DTW is largely used for mining time series but it is not adopted to large data sets because of its quadratic complexity. Global constraints narrow the search path in the matrix...
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