Abstract
In trying to find the features and patterns within the stock time series, time series segmentation is often required as one of the fundamental components in stock data mining. In this paper, a new stock time series segmentation algorithm is proposed. This proposed segmentation method contributes to containing both the important data points and the primitive trends like uptrend and downtrend, while most of the current algorithms only contain one aspect of that. The proposed segmentation algorithm is more efficient and effective in reserving the trends and less complexity than those combined split-and-merge segmentation algorithm. The research result shows that patterns found by using the algorithm and prior to the transaction time impact the stock transaction price. Encouraging experiment is reported from the tests that certain patterns appear most frequently before the low transaction price occurrence.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shatkay, H., Zdonik, S.: Approximate Queries and Representations for Large Data Sequences. In: 12th ICDE, pp. 536–545 (1996)
Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: 4th Conference on Foundations of Data Organization and Algorithms, Chicago, USA, pp. 69–84 (1993)
Oliver, J.J., Baxter, R.A., Wallace, C.S.: Minimum message length segmentation. In: The PAKDD, pp. 222–233 (1998)
Keogh, E., Smyth, P.: A probabilistic approach to fast pattern matching in time series databases. In: 3rd International Conference of Knowledge Discovery and Data Mining, pp. 24–20 (1997)
Zhao, Y.C., Zhang, S.C.: Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis. IEEE Trans. Knowl. Data Eng. 18(2), 231–244 (2006)
Fu, T.C., Chung, F.L., Ng, C.M.: Financial Time Series Segmentation based on Specialized Binary Tree Representation. In: DMIN 2006. 2006 International Conference on Data Mining, Las Vegas Nevada USA, pp. 26–29 (2006)
Chung, F.L., Fu, T.C., Luk, R., Ng, V.: Flexible Time Series Pattern Matching Based on Perceptually Important Points. In: IJCAI Workshop on Learning from Temporal and Spatial Data, pp. 1–7 (2001)
Cheong, F.G.P., Xu, Y.J., Lu, H.: The Predicting Power of Textual Information on Financial Markets. IEEE Intelligent Informatics Bulletin 5(1), 1–10 (2005)
Ge, X., Smyth, P.: Deformable Markov Model Templates for Time-Series Pattern Matching. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston MA, pp. 81–90. ACM Press, New York (2000)
Zhang, Z., Li, J., Wang, S., Wang, H.Q.: Study of Principal Component Analysis on Multi-dimension Stock Data. Chinese Journal of Scientific Instrument 26(8), 2489–2491 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Z. et al. (2007). Pattern Recognition in Stock Data Based on a New Segmentation Algorithm. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-76719-0_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76718-3
Online ISBN: 978-3-540-76719-0
eBook Packages: Computer ScienceComputer Science (R0)