Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| time_series:dtw [2014/12/25 00:49] – [Multidimensional DTW (MD-DTW)] phreazer | time_series:dtw [2014/12/25 01:11] (current) – [Literatur] phreazer | ||
|---|---|---|---|
| Line 4: | Line 4: | ||
| Iterative Deepening Dynamic Time Warping for Time Series | Iterative Deepening Dynamic Time Warping for Time Series | ||
| + | Normalisieren der Distanzen mit der Länge des Warping Path. (Siehe " | ||
| ==== PDTW ==== | ==== PDTW ==== | ||
| Line 25: | Line 26: | ||
| 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. | 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 === | ||
| + | |||
| + | Vergleich einer Query mit allen anderen Trajektorien problematisch. Trajektorien, | ||
| + | |||
| + | Schema: | ||
| + | 1. Segmentieren der ZR in MBRs, speichern in R-Baum | ||
| + | 2. Gegeben Anfrage | ||
| + | 3. Der MBE wird zerteilt in MBRs, die in Index gespeichert sind. | ||
| + | 4. Basierend auf den MBR Überschneidungen werden Ähnlichkeitsschätzungne berechnet. Nur für hinreichend ähnliche ZR werden Distanzen berechnet. | ||