Abstract
China has accumulated a large amount of valuable hydrological data, and the descriptive physical variables can be categorized into various types of hydrological time series. Time-series data usually contains huge amounts of high-dimension data that being continuously updated, thus it is difficult to directly mine the original time series data. This chapter adopts a time series segmentation algorithm based on series importance point (SIP)—PLR_SIP, to approximately describe time series with line segments based on SIP. SIPs are used as the splitting point to reflect the main features of time series and reduce the dimensions of the time series data, thus minimizing the overall error.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldonado M, Chang C-CK, Gravano L, Paepcke A (1997) The Stanford digital library metadata architecture. Int J Digit Libr 1:108–121
Bruce KB, Cardelli L, Pierce BC (1997) Comparing object encodings. In: Abadi M, Ito T (eds) Theoretical aspects of computer software. Lecture notes in computer science, Vol 1281. Springer, Berlin, pp 415–438
van Leeuwen J (ed) (1995) Computer science today. Recent trends and developments. Lecture notes in computer science, Vol 1000. Springer, Berlin
Wu S-Y (2007) Research and application of pattern mining on hydrological time series [D]. Master’s degree thesis, Hohai University
Liu Deping J (1991) Hydrological time series models and forecasting methods [M]. Hohai University Press, Nanjing
Keogh E, Chakrabarti K, Pazzani M et al (2001) Dimensionality reduction for fast similarity search in large time series databases [J]. J Knowl Inf Syst 3(3):263–286
Qu Y, Wang C (1998) Supporting fast search in time Series for movement patterns in multiples scales [C]. In: Proceedings Of the 7th ACM CIKM International conference on information and knowledge management, Bethesda
Keogh E, Pazzani M (1998) An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback [C]. In: Proceedings of the 4th International conference on knowledge discovery and data mining, New York
Park S, Lee D (1990) Fast retrieval of similar subsequences in long sequence databases [C]. In: Proceedings of the 3rd IEEE knowledge and data engineering exchange workshop, Chicago
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer Science+Business Media B.V.
About this paper
Cite this paper
Chen, H. (2012). The Application of Series Importance Points (SIP) Based Partition Method on Hydrological Data Processing. In: He, X., Hua, E., Lin, Y., Liu, X. (eds) Computer, Informatics, Cybernetics and Applications. Lecture Notes in Electrical Engineering, vol 107. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1839-5_149
Download citation
DOI: https://doi.org/10.1007/978-94-007-1839-5_149
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-1838-8
Online ISBN: 978-94-007-1839-5
eBook Packages: EngineeringEngineering (R0)