Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting time series motifs under uniform scaling
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Indexable PLA for efficient similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Parallel exact time series motif discovery
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
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Finding motifs of financial data streams in real time is a very interesting and valuable work. We hope to find the motif existing in financial data streams on local trend subsequence. A stock market trader might use such a tool to spot arbitrage opportunities or escape the underlying venture. The paper introduces a novel distance measurement, that is SDD (Slope Duration Distance), for local subsequences. At the same time, we propose an efficient algorithm of motif discovery over a great deal of financial data streams, that is PMDGS (P-Motif Discovery based on Grid Structure), which make use of PLA (Piecewise Linear Approximation) technology and grid structure. Extensive experiments on synthetic data and real world financial trading data show that our model provides several orders of magnitude performance improvement relative to traditional naive linear scan techniques.