time_series:dtw

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Dynamic Time Warping

Literatur Iterative Deepening Dynamic Time Warping for Time Series

Dynamic Time Warping mit Piecewise Aggregate Approximation (PAA): Approximieren einer Zeitreihe durch Segmentierung in gleichlange Teile und Mittelwert der Datenpunkte innerhalb dieser Punkte.

Zuerst Normalisieren jeder Dimension von t und r.

$$d(i,j) = \sum_{k=1}^K(t(k,i)-r(k,j))^2$$

Gleichzeitiges Alignment mehrerer Zeitreihen

Quelle: Multiple Multidimensional Sequence Alignment Using Generalized Dynamic Time Warping

A Scalable Method for Time Series Clustering

Ersetzen der Punkte durch globale charakteristische Measures.

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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  • Last modified: 2014/12/25 00:49
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