Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
A real time hybrid pattern matching scheme for stock time series
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
A review on time series data mining
Engineering Applications of Artificial Intelligence
An adaptive algorithm for online time series segmentation with error bound guarantee
Proceedings of the 15th International Conference on Extending Database Technology
An adaptive approach for online segmentation of multi-dimensional mobile data
MobiDE '12 Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access
Symbolic representation of smart meter data
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Multi-scale dissemination of time series data
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise Linear Approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.