Open Access Open Access  Restricted Access Subscription Access


Fei Hao, Ling Hei Yeung


This paper is to apply Rough Set to data mining of time series. Firstly, we process the time series data by attribute selection and similarity sequence search. Secondly, the time series is partitioned into some sets of pattern by Mobile Window Method (MWM) and each pattern is a trend of time series. Thirdly, an information table is made by predicting attributes and targeting attribute in trending variation ratio structure sequence (TVRSS). Then, the original information table is made suitably for rough set to discover knowledge. Finally, the extracting rules can predict the time series behavior in the future. The total process is four steps. In the end, we show some examples to demonstrate our method on the time series data of stock market.


TVRSS; Time series; Rough Set; Prediction.

Full Text:



Shao Fengjing, Yu Zhongqing. Principle and Algorithm of Data Mining, Water conservancy & water electric press of China, Beijing, 2003.

P.Lee J, S.Kim. “Trend Similarity and Prediction in Time-series Databases [A]”. In: Proc. of SPIE on Data Mining and Knowledge Discovery: Theory, Tools, and Technology II. Washington: SPIE, 2000,pp.201-212

R.Agrawal, C.Faloutsos, A.Swami. “Efficient Similarity Search in Sequence Database”. Springer Verlag, 1993, pp.69-84.

G.Das, D.Gunopulous, H.Mannila. “Finding Similar Time Series”, In: Proc. of 1st European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD97), Komorowski J, Zytkow J (Eds.), 1997.

Z.Stefan. Data Mining for Prediction. Financial Series Case. Doctoral thesis, Department of Computer and System Sciences. The Royal Institute of Technology, 2003.

R.J.Bayardo Jr., R.Agrawal. “Mining the Most Interesting Rules”, In: Proc. of the 5th ACM SIGKDD Inter. Conf. on Knowledge Discovery and Data Mining, August 1999.

Yong Wang, Time-series data mining study and its application in prediction of water quality. Doctor degree dissertation, Department of automatization. Guangdong university of technology, 2005.

Z. Pawlak, “Rough sets”, International Journal of Computer and Information Sciences, vol.11, 1982, pp.341-356.

J.K.Baltzersen. An attempt to predict stock data: a rough sets approach. Diploma thesis, Konwledge Systems Group, Department of Computer Systems and Telematics, The Norwegian Institute of Technology, University of Trondheim, 1996.

Keyun Hu, Research and design of knowledge discovery system based on rough set, HeFei university of technology, 1998.


  • There are currently no refbacks.