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--- time_series:dtw [2014/08/07 14:10] – phreazer
+++ time_series:dtw [2014/12/25 01:05] – [Literatur] phreazer
@@ Line 4: / Line 4: @@
 Iterative Deepening Dynamic Time Warping for Time Series
+Normalisieren der Distanzen mit der Länge des Warping Path. (Siehe "Indexing Multi-Dimensional Time-Series").
 ==== PDTW ====
 Dynamic Time Warping mit Piecewise Aggregate Approximation (PAA):
 Approximieren einer Zeitreihe durch Segmentierung in gleichlange Teile und Mittelwert der Datenpunkte innerhalb dieser Punkte.
+==== Multidimensional DTW (MD-DTW) ====
+Zuerst Normalisieren jeder Dimension von t und r.
+$$d(i,j) = \sum_{k=1}^K(t(k,i)-r(k,j))^2$$
+==== Multiple Multidimensional DTW ====
+Gleichzeitiges Alignment mehrerer Zeitreihen
+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.
+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.
+=== Indexing ===