Multiresolution Motif Discovery in Time Series

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V – Experimental Analysis Scalability Experiments • Dataset: • Reproduced from Mueen et al., 2009*. • 10 different sets of random walk time series • Each set with 10000 up to 100000 series of length 1024 • About 8GB of time series data • We compare MrMotif to Random Projection (Chiu et al., 2003) • Due to its popularity • Is the basis of many current motif discovery approaches • We also compare Space-Saving (SS) and Full Memory (FM) versions of MrMotif Nuno Castro and Paulo Azevedo 04/30/2010 **Ref
V – Experimental Analysis – Scalability Experiments Execution time • Algorithms are executed 10 times for each of the ten increasingly larger datasets • Execution times for each dataset are averaged • Top-10 motifs are recorded • Maximum amount of memory set to 128MB 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 and 6: III - Related work limitations •
Page 7 and 8: IV - Our algorithm • We propose a
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: V - Experimental Analysis • Scala
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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