时间序列表示是时间序列挖掘的一个基础和关键问题。对当前出现的各种典型的时间序列表示方法进行了综述,对各自的特点从多个角度进行了比较研究。结果说明,大部分时间序列表示方法将时间序列降维,且都与应用领域紧密相关,在实际构建系统时仍需对各种表示方法按照实际需求进
参考文献
[1] Agrawal, R., Faloutsos, C. and Swami, A. Efficient similarity search in sequence in
sequence databases. In: Proc. 4th International Conference on Foundations of Data Organization and Algorithms, Chicago, Illinois, USA, 1993. 69~84.
[2] Agrawal, R., Psaila, E. L. Wimmers and M. Zait. Querying shapes of histories. In Proc.
of the 21st International Conference on Very Large Databases(VLDB), 1995. 502~514.
[3] Agrawal, R., K. I. Lin, H.S. Sawhney, et al. Fast similarity search in the presence of
noise, scaling , and translation in time-series databases. In: Proc. of the 21st International Conference on Very Large Databases(VLDB), 1995. 490~500.
[4] Chakrabarti, K., Ortega-Binderberger. M. Porkaew, et al. Similar shape retrieval in
MARS. In: Proc. of IEEE International Conference on Multimedia and Expo.2000.
[5] Chan, K. and Fu, A. W. Efficient time series matching by wavelets. In: Proc. 15th IEEE
International Conference on Data Engineering (ICDE). Sydney, Australia, 1999.126~133.
[6] Chang-Shing Perng, Haixun Wang, and Sylvia R. Zhang, et al. Landmarks: A New
Model for Similarity-Based Pattern Querying in Time Series Databases. In: Proc. of 16th International Conference of Data Engineering(ICDE), San Diego, USA.2000.
[7] Faloutsos, C., Ranganathan, M., Manolopoulos, Y. Fast subsequence matching in
time-series databases. In: Proc. of ACM SIGMOD Conference, Mineapolis, 1994. 419~429.
[8] H. Shatkay, H. and S. Zdonik. Approximate queries and representations for large data
sequences. In: Proc. 12th International Conference on Data Engineering(ICDE), 1996. 536~545.
[9] Li Quanzhong, Vega Lopez I.F., Moon B. Skyline Index for Time Series Data. IEEE
Trans. on Knowledge and Data Mining, 16(6), 2004. 669~684.
[10] Li Wei, Kumar N., Lolla V, et al. A Practical Tool for Visualizing and Data Mining
Medical Time Series. In: Computer-Based Medical Systems, 2005.
[11] Jessica Lin, Keogh E. Londardi S, et al. A Symbolic Representation of Time Series, with
Implications for Streaming Algorithms.In: Proc. of the 8th ACM SIGMOD workshop on Research(DMKD), San Diego, CA, USA. 2003.
[12] Jessica Lin, Keogh E, Londardi S. Visualizing and Discovering Non-Trivial Patterns in
Large Time Series Databases.In: Information Visualization, 2005.
[13] Kamel, I. and Faloutsos, C. Hilbert R-tree: An improved R-tree using fractals. In: Proc.
Very Large Database (VLDB), 1994. 500~509.
[14] Keogh E. Exact indexing of dynamic time warping. In: Proc. of 28th International
Conference on Very Large Databases Conference(VLDB), Hong Kong, China.2002. 406~417.
[15] Keogh E., Kasetty, S. On the need for time series data mining benchmarks: a survey and
empirical demonstration. In: 8th ACM SIGKDD International Conf. on Knowledge Discovery and Data Mining, Edmonton, Canada, 2002. 102~111.
[16] Keogh E. Pazzani, M. A simple dimensionality reduction technique for fast similarity
search in large time series databases. In: 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kyoto, Japan,2000. 122~133.
[17] Keogh E. Chakrabarti K, Pazzani M. Locally adaptive dimensionality reduction for
indexing large time series databases. In: Proc. of ACM SIGMOD Conference on Management of Data, 2001. 151~162.
[18] Keogh E. Similarity Search in Massive Time Series Databases: [Ph.D. Thesis].
University of California, Irvine. 2002.(Importance)
[19] Keogh E. Lonardi S. Ratanamahatana A.C. Towards Parameter-Free Data Mining. In:
Proc. of KDD, Seattle, WA. USA. 2004.
[20] Keogh E. Selina Chu, David Hart, et al. An Online Algorithm for Segmenting Time
Series. In: Proc. of IEEE International Conference on Data Mining(ICDM), 2001.
[21] Kevin B. Pratt and Eugene Fink. Search for Patterns in Compressed Time Series. In: International Journal of Image and Graphics. 2(1), 2002. 89~106.
- 5 -
百度搜索“77cn”或“免费范文网”即可找到本站免费阅读全部范文。收藏本站方便下次阅读，免费范文网，提供经典小说教育文库时间序列表示进展及比较研究时间序列挖掘建模环境(5)在线全文阅读。
相关推荐：