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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 603629, 7 pages
Piecewise Trend Approximation: A Ratio-Based Time Series Representation
1College of Computer Science, Chongqing University, Chongqing 400044, China
2School of Automation, Chongqing University, Chongqing 400044, China
3Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259 Nagatsuta, Midoriku, Yokohama 226-8502, Japan
Received 13 March 2013; Accepted 27 April 2013
Academic Editor: Fuding Xie
Copyright © 2013 Jingpei Dan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- F. Gullo, G. Ponti, A. Tagarelli, and S. Greco, “A time series representation model for accurate and fast similarity detection,” Pattern Recognition, vol. 42, no. 11, pp. 2998–3014, 2009.
- Y. Ding, X. Yang, A. J. Kavs, and J. Li, “A novel piecewise linear segmentation for time series,” in Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE '10), pp. 52–55, February 2010.
- K. Huarng and T. H. K. Yu, “Ratio-based lengths of intervals to improve fuzzy time series forecasting,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, no. 2, pp. 328–340, 2006.
- K. Chan and A. Fu, “Efficient time series matching by wavelets,” in Proceedings of the International Conference on Data Engineering (ICDE '99), pp. 126–133, 1999.
- Y. L. Wu, D. Agrawal, et al., “A comparison of DFT and DWT based similarity search in time-series databases,” in Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM '00), pp. 488–495, 2000.
- E. H. Bristol, “Swinging door trending: adaptive trending recording,” in Proceedings of the ISA National Conference, pp. 749–753, 1990.
- T. Pavlidis and S. L. Horowitz, “Segmentation of plane curves,” IEEE Transactions on Computers, vol. 23, no. 8, pp. 860–870, 1974.
- E. Keogh and M. Pazzani, “An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback,” in Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD '98), pp. 239–241, 1998.
- E. Keogh, K. Chakrabarti, M. Pazzani, and S. Mehrotra, “Dimensionality reduction for fast similarity search in large time series databases,” Knowledge and Information Systems, vol. 3, pp. 263–286, 2001.
- E. Keogh and M. Pazzani, “Scaling up dynamic time warping for data mining applications,” in Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (KDD '00), pp. 285–289, 2000.
- X. Feng, C. Cheng, L. Changling, and S. Huihe, “An improved process data compression algorithm,” in Proceedings of the International Conference on Intelligent Control and Automation, pp. 2190–2193, 2002.
- K. Chakrabarti, E. Keogh, S. Mehrotra, and M. Pazzani, “Locally adaptive dimensionality reduction for indexing large time series databases,” ACM Transactions on Database Systems, vol. 27, no. 2, pp. 188–228, 2002.
- J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A symbolic representation of time series, with implications for streaming algorithms,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (DMKD '03), pp. 2–11, 2003.
- C. S. Burrus, R. A. Gopinath, and H. Guo, Introduction to Wavelets and Wavelet Transforms, a Primer, Prentice-Hall, Englewood Cliffs, NJ, USA, 1997.
- F. Korn, H. V. Jagadish, and C. Faloutsos, “Efficiently supporting Ad Hoc queries in large datasets of time sequences,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 289–300, 1997.
- K. V. Kanth, D. Agrawal, and A. Singh, “Dimensionality reduction for similarity searching in dynamic databases,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 166–176, 1998.
- D. Rafiei and A. Mendelzon, “Similarity-based queries for time series data,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 13–25, 1997.
- D. Rafiei and A. Mendelzon, “Efficient retrieval of similar time sequences using DFT,” in Proceedings of the International Conference on Foundations of Data Organization and Algorithms (FODO '98), pp. 249–257, 1998.
- J. C. Mason and D. C. Handscomb, Chebyshev Polynomials, Chapman & Hall/CRC, Boca Raton, Fla, USA, 2003.
- Y. Cai and R. Ng, “Indexing spatio-temporal trajectories with Chebyshev polynomials,” in Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 599–610, 2004.
- G. Reinert, S. Schbath, and M. S. Waterman, “Probabilistic and statistical properties of words: an overview,” Journal of Computational Biology, vol. 7, no. 1-2, pp. 1–46, 2000.
- E. Keogh and S. Kasetty, “On the need for time series data mining benchmarks: a survey and empirical demonstration,” Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 349–371, 2003.
- D. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series,” in Proceedings of the Workshop on Knowledge Discovery in Databases, at the 12th International Conference on Artificial Intelligence, pp. 359–370, 1994.
- J. McQueen, “Some methods for classification and analysis of multivariate observation,” in Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297, 1967.
- A. Nanopoulos, R. Alcock, and Y. Manolopoulos, “Feature-based classification of time-series data,” in Information Processing and Technology, pp. 49–61, Nova Science Publishers, Commack, NY, USA, 2001.
- C. A. Ratanamahatana and E. Keogh, “Making time-series classification more accurate using learned constraints,” in Proceedings of the 4th SIAM International Conference on Data Mining (SDM '04), pp. 11–22, April 2004.
- E. Keogh and T. Folias, “The UCR time series data mining archive,” 2002, http://www.cs.ucr.edu/~eamonn/time_series_data/.
- T. M. Mitchell, ,Machine Learning, Computer Sciences Series, McGraw-Hill, New York, NY, USA, 1997.
- T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Transaction on Information Theory, vol. 13, pp. 21–27, 1967.