Improved Linear Dynamical System for Unsupervised Time Series Recognition
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
The paper considers the challenges involved in measuring the similarities between time series, such as time shifts and the mixture of frequencies. To improve recognition accuracy, we investigate an improved linear dynamical system for discovering prominent features by exploiting the evolving dynamics and correlations in a time series, as the quality of unsupervised pattern recognition relies strongly on the extracted features. The proposed approach yields a set of compact extracted features that boosts the accuracy and reliability of clustering for time series data. Experimental evaluations are carried out on time series applications from the scientific, socio-economic, and business domains. The results show that our method exhibits improved clustering performance compared to conventional methods. In addition, the computation time of the proposed approach increases linearly with the length of the time series.
Abstract
The paper considers the challenges involved in measuring the similarities between time series, such as time shifts and the mixture of frequencies. To improve recognition accuracy, we investigate an improved linear dynamical system for discovering prominent features by exploiting the evolving dynamics and correlations in a time series, as the quality of unsupervised pattern recognition relies strongly on the extracted features. The proposed approach yields a set of compact extracted features that boosts the accuracy and reliability of clustering for time series data. Experimental evaluations are carried out on time series applications from the scientific, socio-economic, and business domains. The results show that our method exhibits improved clustering performance compared to conventional methods. In addition, the computation time of the proposed approach increases linearly with the length of the time series.
참고문헌 (27)
- O. Lange Wishmuller, "Cluster analysis of biomedical image time-series", International Journal of Computer Vision, 46(2), pp.103~128 (2002)
- C. S. Moller Levet, "Fuzzy clustering of short-time series and unevenly distributed sampling points", In lecture notes in computer science, 2811, pp.330~340 (2003)
- S. Changdrakala, "A density based method for multivariate time series clustering in kernel feature space", In proceedings of IEEE international joint conference on neural networks, pp.1885~1890 (2008)
- K. Kalpakis, "Distance measure for effective clustering of ARIMA time series", In Proceedings of the 2001 IEEE International Conference on Data Mining, pp.273~280 (2001)
- H. S. Guan, "Cluster financial time series for portfolio", In proceedings of international conference on wavelet analysis and pattern recognition, pp.851~856 (2007)
- M. Gavrilov, "Mining the stock market: which measure is best?", in:Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.487~496 (2000)
- X. Zhang, "A novel clustering method on time series data", Expert Systems with Applications, 38(9), pp.11891~11900 (2011)
- T. Warren Liao, "Clustering of time series data- a survey", The journal of the pattern recognition, 38, pp.1857~1874 (2005)
- Ding, "K-means clustering via principal component analysis", In Proceedings of the twenty-first international conference on Machine learning, pp.29~32 (2004)
- I. T. Jolliffe, "Principal Component Analysis" (2002), Springer
- L. Li, "Time Series Clustering:Complex is Simple!", In Proceedings of the 28th International Conference of Machine Learning (2011)
- M. Jahangiri, "Shift-split:I", O efficient maintenance of wavelet-transformed multidimensional data, 2005, 275~286
- Time series feature extraction for data mining using dwt and dft, "F. Morchen", Philipps-University Marburg, ~, pp.33 (2003)
- L. Li, "Parsimonious Linear Fingerprint for Time Series", Proceedings of the VLDB Endowment, 3, pp.385~396 (2010)
- L. Ralaivola, "Time series Filtering, Smoothing and Learning using the Kernel Kalman Filter", In Proceedings of International Joint Conference on Neural Network (2005)
- A. Jain, "Adaptive stream resource management using Kalman Filter", In SIGMOD, pp.11~22 (2004)
- D. Gunopulos, "Time series similarity measures and time series indexing", In SIGMOD Conference (2001)
- K. Deng, "Learning to recognize time series: Combining arma models with memory-based learning", Proc. of the International Symposium on Computational Intelligence in Robotics and Automation, pp.246~250 (1997)
- X. Wang, "Characteristic-Based Clustering for Time Series Data", Data Mining and Knowledge Discovery, 13, pp.335~364 (2006)
- N. T. Ngoc Anh, "Hidden Dynamic Learning for Long-Interval Consecutive Missing Values Reconstruction in EEG Time Series", In Proceedings of 2011 IEEE International Conference on Granular Computing, pp.653~658 (2011)
- L. Li, "Dynammo: Mining and summarization of coevolving sequences with missing values", In KDD, ACM (2009)
- R. E. Kalman, "A new approach to linear filtering and prediction problems", Transactions of the ASME C Journal of Basic Engineering, pp.25~45 (1960)
- R. H. Shumway, "An approach to time series smoothing and forecasting using the EM algorithm", Journal of Time Series Analysis, pp.253~264 (1982)
- Parameter estimation for linear dynamical systems, "Z. Ghahramani", ~, pp.CRGTR-96-2 (1996)
- E. Keogh, "The UCR time series data mining archive", ~ (2002)
- Y. Xiong, "Time series clustering with ARMA mixtures", Pattern Recognition, 37(8), pp.1675~1689 (2004)