Abstract
The method of recognition of shape association patterns with direct and inverse relationships is proposed. This method is based on a new time series shape association measure based on Up and Down trend associations. The application of this technique to analysis of associations between well production data in petroleum reservoirs is discussed.
Chapter PDF
Similar content being viewed by others
References
Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search in Sequence Databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993)
Agrawal, R., Lin, K.-I., Sawhney, H.S., Shim, K.: Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: 21st International Conference on Very Large Databases, pp. 490–501. Morgan Kaufmann, San Francisco (1995)
Batyrshin, I., Herrera-Avelar, R., Sheremetov, L., Panova, A.: Moving approximation transform and local trend associations in time series data bases. In: Batyrshin, I., Kacprzyk, J., Sheremetov, L., Zadeh, L. (eds.) Perception-based Data Mining and Decision Making in Economics and Finance. SCI, vol. 36, pp. 55–83. SpringerPhysicaVerlag (2007)
Batyrshin, I., Sheremetov, L., Velasco-Hernandez, J.X.: On axiomatic definition of time series shape association measures. In: Workshop on Operations Research and Data Mining, ORADM 2012, Cancun, pp. 117–127 (2012)
Batyrshin, I., Bulgakov, I., Hernandez, A.-L., Huitron, C., Chi, M., Raimundo, A., Cosultchi, A.: VMD-Petro®: Visualization and data mining tool for oilfields. In: Workshop on Operations Research and Data Mining, ORADM 2012, Cancun, pp. 140–148 (2012)
Batyrshin, I.Z., Sheremetov, L.B.: Perception-based approach to time series data mining. Applied Soft Computing 8, 1211–1221 (2008)
Chatfield, C.: The Analysis of Time Series: An Introduction. Chapman and Hall (1984)
Das, G., Gunopulos, D.: Time series similarity and indexing. In: Handbook on Data Mining, pp. 279–304. Lawrence Erlbaum Associates (2003)
Fu, T.-C.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24, 164–181 (2011)
Goldin, D.Q., Kanellakis, P.C.: On Similarity Queries for Time-Series Data: Constraint Specification and Implementation. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 137–153. Springer, Heidelberg (1995)
Kacprzyk, J., Wilbik, A., Zadrozny, S.: Linguistic summarization of trends: a fuzzy logic based approach. In: 11th Int. Conf. Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2006, pp. 2166–2172 (2006)
Last, M., Kandel, A., Bunke, H.: Data Mining in Time Series Databases, Machine Perception and Artificial Intelligence, vol. 57. World Scientific (2004)
Liao, T.W.: Clustering of time series data – A survey. Pattern Recognition 38, 1857–1874 (2005)
Möller-Levet, C.S., Klawonn, F., Cho, K.-H., Wolkenhauer, O.: Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points. In: Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 330–340. Springer, Heidelberg (2003)
Rafiei, D., Mendelzon, A.O.: Querying time series data based on similarity. IEEE Transactions on Knowledge and Data Engineering 12, 675–693 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Batyrshin, I. (2012). Up and Down Trend Associations in Analysis of Time Series Shape Association Patterns. In: Carrasco-Ochoa, J.A., MartÃnez-Trinidad, J.F., Olvera López, J.A., Boyer, K.L. (eds) Pattern Recognition. MCPR 2012. Lecture Notes in Computer Science, vol 7329. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31149-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-31149-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31148-2
Online ISBN: 978-3-642-31149-9
eBook Packages: Computer ScienceComputer Science (R0)