Multiresolution Motif Discovery in Time Series

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III – Related work limitations (cont.) • Consider motifs at a single resolution • Are not suited to interactivity • Large number of unintuitive parameters to set: • Motif length • Range (distance threshold) • Number of columns in the subsequence matrix • Limited to finding motifs in univariate time series Nuno Castro and Paulo Azevedo 04/30/2010
IV – Our algorithm • We propose an algorithm: • Multiresolution Motif Discovery in Time Series: MrMotif • Time efficient: • One single sequential disk scan • Clever representation technique (iSAX) • Use of constant access time structures • Memory efficient: • Combine our approach with the Space-Saving algorithm • Adjustable amount of memory to use Nuno Castro and Paulo Azevedo 04/30/2010
Page 1 and 2: Tenth SIAM International Conference
Page 3 and 4: I - Motif Definition • Motifs, al
Page 5: III - Related work limitations •
Page 9 and 10: IV - Our algorithm - Problem defini
Page 11 and 12: IV - Our algorithm Problem definiti
Page 13 and 14: IV - Our algorithm Properties • M
Page 15 and 16: IV - Our algorithm - Properties Mul
Page 17 and 18: IV - Our algorithm - Properties Spa
Page 19 and 20: V - Experimental Analysis • Scala
Page 21 and 22: V - Experimental Analysis - Scalabi
Page 23 and 24: V - Experimental Analysis - Scalabi
Page 25 and 26: V - Experimental Analysis Experimen
Page 27 and 28: VI - Conclusions • We have introd
Page 29 and 30: Thank you for your attention! • C
Page 31: On similarity Nuno Castro and Paulo

Multiresolution Motif Discovery in Time Series

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