SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
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
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
SAXually Explicit Images: Finding Unusual Shapes
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Spectral similarity for analysis of DNA microarray time-series data
International Journal of Data Mining and Bioinformatics
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Real-Time Classification of Streaming Sensor Data
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
iSAX 2.0: Indexing and Mining One Billion Time Series
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
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A long standing challenge for time series analysis is to develop representation techniques for dimension reduction while still preserving their fundamental features. As an effective representation technique, Symbolic Aggregate Approximation (SAX) has been widely used for dimension reduction in time series analysis. However, SAX always maps time series data into symbols by definite breakpoints. As a result, the similar points close to the breakpoints cannot be well represented, and thus lead to poor Tightness of Lower Bounds (TLB). To fill this crucial void, in this paper, we develop a time series representation method, named Random Shifting based SAX (rSAX), which has the ability in significantly improving the TLB of representations without increasing the corresponding granularity of representations. Specifically, the key idea of rSAX is to generate a group of breakpoints by random shifting rather than definite breakpoints. Therefore, the points close to each other will have higher probabilities to be mapped into the same symbols, while the points far away from each other will have higher probabilities to be mapped into different symbols. In addition, we also theoretically prove that rSAX can achieve better mapping performances and TLB than SAX. Finally, extensive experiments on several real-world data sets clearly validate the effectiveness and efficiency of the rSAX approach.