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Erschienen in:
2012 | OriginalPaper | Buchkapitel
TSX: A Novel Symbolic Representation for Financial Time Series
verfasst von : Guiling Li, Liping Zhang, Linquan Yang
Erschienen in: PRICAI 2012: Trends in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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Existing symbolic approaches for time series suffer from the flaw of missing important trend feature, especially in financial area. To solve this problem, we present Trend-based Symbolic approximation (TSX), based on Symbolic Aggregate approximation (SAX). First, utilize Piecewise Aggregate Approximation (PAA) approach to reduce dimensionality and discretize the mean value of each segment by SAX. Second, extract trend feature in each segment by recognizing key points. Then, design multiresolution symbolic mapping rules to discretize trend information into symbols. Experimental results show that, compared with traditional symbol approach, our approach not only represents the key feature of time series, but also supports the similarity search effectively and has lower false positives rate.