Efficient Similarity Search In Sequence Databases
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ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
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PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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Pattern Recognition Letters
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ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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IEEE Transactions on Neural Networks
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Expert Systems with Applications: An International Journal
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Principal component analysis (PCA) is often applied to dimensionality reduction for time series data mining. However, the principle of PCA is based on the synchronous covariance, which is not very effective in some cases. In this paper, an asynchronism-based principal component analysis (APCA) is proposed to reduce the dimensionality of univariate time series. In the process of APCA, an asynchronous method based on dynamic time warping (DTW) is developed to obtain the interpolated time series which derive from the original ones. The correlation coefficient or covariance between the interpolated time series represents the correlation between the original ones. In this way, a novel and valid principal component analysis based on the asynchronous covariance is achieved to reduce the dimensionality. The results of several experiments demonstrate that the proposed approach APCA outperforms PCA for dimensionality reduction in the field of time series data mining.