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| time_series:dtw [2014/12/25 00:04] – [Dynamic Time Warping] phreazer | time_series:dtw [2014/12/25 00:11] (current) – [Literatur] phreazer | ||
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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. | 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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| + | Vergleich einer Query mit allen anderen Trajektorien problematisch. Trajektorien, | ||
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| + | 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. | ||