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time_series:dtw [2014/08/07 14:07] – angelegt phreazer | time_series:dtw [2014/12/25 01:05] – [Literatur] phreazer | ||
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====== Dynamic Time Warping ====== | ====== Dynamic Time Warping ====== | ||
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+ | Literatur | ||
+ | Iterative Deepening Dynamic Time Warping for Time Series | ||
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+ | Normalisieren der Distanzen mit der Länge des Warping Path. (Siehe " | ||
+ | ==== PDTW ==== | ||
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+ | Dynamic Time Warping mit Piecewise Aggregate Approximation (PAA): | ||
+ | Approximieren einer Zeitreihe durch Segmentierung in gleichlange Teile und Mittelwert der Datenpunkte innerhalb dieser Punkte. | ||
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+ | ==== Multidimensional DTW (MD-DTW) ==== | ||
+ | Zuerst Normalisieren jeder Dimension von t und r. | ||
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+ | $$d(i,j) = \sum_{k=1}^K(t(k, | ||
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+ | ==== Multiple Multidimensional DTW ==== | ||
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+ | Gleichzeitiges Alignment mehrerer Zeitreihen | ||
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+ | Quelle: Multiple Multidimensional Sequence Alignment Using Generalized Dynamic Time Warping | ||
+ | ==== Literatur ==== | ||
+ | === A Scalable Method for Time Series Clustering === | ||
+ | Ersetzen der Punkte durch globale charakteristische Measures. | ||
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+ | Dynamic Time Warping (DTW) has been applied in time series mining to resolve the difficulty caused when clustering time series of varying lengths in Euclidean space or containing possible out-of-phase similarities (Berndt & Clifford, 1994; Keogh, 2002; Ratanamahatana & Keogh, 2004). [...] but is not defined if a single data point is missing. | ||
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+ | === Indexing === | ||
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