Abstract
In this paper, we propose a new technique for time-series prediction. Here we assume that time-series data occur depending on event which is unobserved directly, and we estimate future data as output from the most likely event which will happen at the time. In this investigation we model time-series based on event sequence by using Hidden Markov Model(HMM), and extract time-series patterns as trained HMM parameters. However, we can’t apply HMM approach to data stream prediction in a straightforward manner. This is because Baum-Welch algorithm, which is traditional unsupervised HMM training algorithm, requires many stored historical data and scan it many times. Here we apply incremental Baum-Welch algorithm which is an on-line HMM training method, and estimate HMM parameters dynamically to adapt new time-series patterns. And we show some experimental results to see the validity of our method.
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
Cavalin, P.R., Sabourin, R., Suen, C.Y., Britto Jr., A.S.: Evaluation of Incremental Learning Algorithms for An HMM-Based Handwritten Isolated Digits Recognizer. In: The 11th International Conference on Frontiers in Handwriting Recognition (ICFHR 2008), Montreal, August 19-21 (2008)
Duan, S., Babu, S.: Processing forecasting queries. In: Proc. of the 2007 Intl. Conf. on Very Large Data Bases (2007)
Gelper, S., Fried, R., Croux, C.: Robust Forecasting with Exponential and Holt-Winters Smoothing (June 2007)
de Gooijer, J.G., Hyndman, R.J.: 25 Years of IIF Time Series Forecasting: A Selective Review, Tinbergen Institute Discussion Papers 05-068/4 (2005)
Md. Hassan, R., Nath, B.: StockMarket Forecasting Using Hidden Markov Model: A New Approach. In: Proceedings of the 5th International Conference on Intelligent Systems Design and Applications (2005)
Jian, N., Gruenwald, L.: Research Issues in Data Stream Association Rule Mining, SIGMOD Record 35-1, pp.14–19 (2006)
Iwasaki, M.: Statistic Analysis for Incomplete Data. EconomistSha, Inc. (2002) (in Japanese)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Mitchell, T.: Machine Learning. McGrawHill Companies (1997)
Muthukrishnan, S.: Data streams: algorithms and applications. In: Proceedings of the fourteenth annual ACM-SIAM symposium on discrete algorithms (2003)
Stenger, B., Ramesh, V., Paragios, N.: Topology free Hidden Markov Models: Application to background modeling. In: IEEE International Conference on Computer Vision (2001)
Wakabayashi, K., Miura, T.: Identifying Event Sequences using Hidden Markov Model. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 84–95. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wakabayashi, K., Miura, T. (2009). Data Stream Prediction Using Incremental Hidden Markov Models. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2009. Lecture Notes in Computer Science, vol 5691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03730-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-03730-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03729-0
Online ISBN: 978-3-642-03730-6
eBook Packages: Computer ScienceComputer Science (R0)