Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Adaptive query processing for time-series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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A Multiresolution Symbolic Representation of Time Series
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SFA: a symbolic fourier approximation and index for similarity search in high dimensional datasets
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Significant motifs in time series
Statistical Analysis and Data Mining
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The VLDB Journal — The International Journal on Very Large Data Bases
Searching and mining trillions of time series subsequences under dynamic time warping
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Uncertain time-series similarity: return to the basics
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Time series discord discovery using WAT algorithm and iSAX representation
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Current research in indexing and mining time series data has produced many interesting algorithms and representations. However, the algorithms and the size of data considered have generally not been representative of the increasingly massive datasets encountered in science, engineering, and business domains. In this work, we show how a novel multi-resolution symbolic representation can be used to index datasets which are several orders of magnitude larger than anything else considered in the literature. Our approach allows both fast exact search and ultra fast approximate search. We show how to exploit the combination of both types of search as sub-routines in data mining algorithms, allowing for the exact mining of truly massive real world datasets, containing millions of time series.