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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.
Temporal databases, Data mining, Mining methods and algorithms, 0Information Storage

X. Liu, H. Wang and Z. Lin, "Novel Online Methods for Time Series Segmentation," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 1616-1626, 2008.
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