Proceedings of the International Multiconference on ISBN 978-83-60810-14-9 Computer Science and Information Technology, pp. 195 – 200 ISSN 1896-7094 Abstract—In this paper our method of discovering data se- quences in the time series is presented. Two major approaches to this topic are considered. The first one, when we need to judge whether a given series is similar to any of the known pat- terns and the second one when there is a necessity to find how many times within long series a defined pattern occurs. In both cases the main problem is to recognize pattern occurrence(s), but the distinction is essential because of the time frame within which identification process is carried on. The proposed method is based on the usage of multilayered feed-forward neural net- work. Effectiveness of the method is tested in the domain of fi- nancial analysis but its adaptation to almost any kind of se- quences data can be done easily. I. INTRODUCTION HE issue of discovering data sequences has been heav- ily investigated by the scientists of different disciplines for many years. Despite this fact there is no doubt the issue is still up-to-date. Statisticians, economists, weather forecast- ers, operating system administrators – all of them, in their daily routine, deal with many kind of sequences. Specifi- cally, in the domain of finance analysis there are patterns de- fined by the Technical Analysis (TA). Recognition of some of this patterns among quotation data triggers investors buy or sell decisions regarding examined stock. So it is crucial for the people who play the stock exchange to recognize pat- terns when they are really formed by stock exchange quota- tions. Because of that there is a need to provide trustworthy method of finding defined sequences. Lately, discovery of patterns in time series plays very important role in the area of bioinformatics [2] also. T In this paper a method of discovering data sequences in the domain of financial analysis is presented but its adapta- tion to any other kind of sequences data can be easily done. This method uses multilayered feed-forward neural network to recognize the technical analysis patterns. All experiments which aim was evaluation of the method efficiency, are done by the use of data which come from the Warsaw Stock Mar- ket. The paper consists of five sections. The next one de- scribes different approaches to the problem of sequence data discovery. Our method is introduced in the third section. The next one presents the results of the experiments. some of them were performed by the use of the method in artificial environment simulating the Warsaw Stock Market. The final section presents conclusion and future plans. II. RELATED WORKS Methods of pattern discovery in time series sequences in the financial analysis are closely connected to econometrics which can shortly be defined as the branch of economy that deals with defining models of different systems by the use of mathematics and statistics. Some of these models are created by economists in order to make analysis of data or to make a prediction of future stock exchange quotations. The problem is to prepare a good model, where ‘good’ means the model which takes into consideration all important relations which can be distinguished in the modeled reality. This is of course not easy. Often some relations become important under cer- tain circumstances when others turn out to be useless. To comply with all defined requirements there is a need to pre- pare accurate model which can consist of even hundreds of equations. Such approach causes difficulties in its compre- hensibility by the user but also in a computer implementa- tion. That is why scientists look for other methods of discov- ering patterns in time series. Fu and others [3] describe a method which uses perceptu- ally important points (PIPs) of the graph to compare it with other graph. By PIPs are assumed points that are significant for the shape of the diagram to which they belong. Authors presented a method for finding PIPs and algorithms for de- termining the distance between points from two different graphs. The idea introduced by them reflects the human-like way of thinking (people usually do not remember all the points which build the graph – they keep just more signifi- cant ones in mind and then compare them to the other impor- tant points). The advantage of this algorithm is its easy im- plementation. Despite of that fact, there is a big disadvantage of this method of discovering sequences. A problem is with series which have high amplitude between two adjacent points – higher than some PIPs can place between those two points. It leads to the problem, when we have PIPs identified not among whole series but mainly in some its parts. Similar approach is applied in the paper [1], where a special metrics of similarity between a pattern in question and a given pat- tern is designed. 978-83-60810-14-9/08/$25.00 © 2008 IEEE 195 Discovery of Technical Analysis Patterns Urszula Markowska-Kaczmar Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland Email: Urszula.Markowska-Kaczmar@pwr.wroc.pl Maciej Dziedzic Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland Email: 133644@student.pwr.wroc.pl