A time series representation model for accurate and fast similarity detection

Similarity search and detection is a central problem in time series data processing and management. Most approaches to this problem have been developed around the notion of dynamic time warping, whereas several dimensionality reduction techniques have been proposed to improve the efficiency of simil...

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Bibliographic Details
Published in:Pattern recognition 2009-11, Vol.42 (11), p.2998-3014
Main Authors: Gullo, Francesco, Ponti, Giovanni, Tagarelli, Andrea, Greco, Sergio
Format: Article
Language:English
Subjects:
Applied sciences
Classification
Clustering
Coding, codes
Cryptography
Dimensionality reduction
Exact sciences and technology
Information theory
Information, signal and communications theory
Miscellaneous
Representation models
Signal and communications theory
Signal processing
Similarity detection
Telecommunications and information theory
Time series data
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Summary:Similarity search and detection is a central problem in time series data processing and management. Most approaches to this problem have been developed around the notion of dynamic time warping, whereas several dimensionality reduction techniques have been proposed to improve the efficiency of similarity searches. Due to the continuous increasing of sources of time series data and the cruciality of real-world applications that use such data, we believe there is a challenging demand for supporting similarity detection in time series in a both accurate and fast way. Our proposal is to define a concise yet feature-rich representation of time series, on which the dynamic time warping can be applied for effective and efficient similarity detection of time series. We present the Derivative time series Segment Approximation (DSA) representation model, which originally features derivative estimation, segmentation and segment approximation to provide both high sensitivity in capturing the main trends of time series and data compression. We extensively compare DSA with state-of-the-art similarity methods and dimensionality reduction techniques in clustering and classification frameworks. Experimental evidence from effectiveness and efficiency tests on various datasets shows that DSA is well-suited to support both accurate and fast similarity detection.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2009.03.030