Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Subsequence Matching in Time Series Databases Under Time and Amplitude Transformations
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An Advanced Segmental Semi-Markov Model Based Online Series Pattern Detection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Scaling and time warping in time series querying
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Fractals and Scaling In Finance: Discontinuity, Concentration, Risk
Fractals and Scaling In Finance: Discontinuity, Concentration, Risk
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Efficient online detection of similar patterns under arbitrary time scaling of a given time sequence is a challenging problem in the real-time application field of time series data mining. Some methods based on sliding window have been proposed. Although their ideas are simple and easy to realize, their computational loads are very expensive. Therefore, model based methods are proposed. Recently, the segmental semi-Markov model is introduced into the field of online series pattern detection. However, it can only detect the matching sequences with approximately equal length to that of the query pattern. In this paper, an improved segmental semi-Markov model, which can solve this challenging problem, is proposed. And it is successfully demonstrated on real data sets.