Vector quantization and signal compression
Vector quantization and signal compression
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Boolean representation based data-adaptive correlation analysis over time series streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Adaptive correlation analysis in stream time series with sliding windows
Computers & Mathematics with Applications
Time series analysis with multiple resolutions
Information Systems
A review on time series data mining
Engineering Applications of Artificial Intelligence
ACM Computing Surveys (CSUR)
Multiresolution similarity search in time series data: an application to EEG signals
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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Efficiently searching for similarities among time series and discovering interesting patterns is an important and non-trivial problem with applications in many domains. The high dimensionality of the data makes the analysis very challenging. To solve this problem, many dimensionality reduction methods have been proposed. PCA (Piecewise Constant Approximation) and its variant have been shown efficient in time series indexing and similarity retrieval. However, in certain applications, too many false alarms introduced by the approximation may reduce the overall performance dramatically. In this paper, we introduce a new piecewise dimensionality reduction technique that is based on Vector Quantization. The new technique, PVQA (Piecewise Vector Quantized Approximation), partitions each sequence into equi-length segments and uses vector quantization to represent each segment by the closest (based on a distance metric) codeword from a codebook of key-sequences. The efficiency of calculations is improved due to the significantly lower dimensionality of the new representation. We demonstrate the utility and efficiency of the proposed technique on real and simulated datasets. By exploiting prior knowledge about the data, the proposed technique generally outperforms PCA and its variants in similarity searches.