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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bo Zhou Jinglu Hu |
| Copyright Year | 2009 |
| Description | Author affiliation: Graduate School of Information, Production and System, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu-shi, Fukuoka, JAPAN (Bo Zhou; Jinglu Hu) |
| Abstract | Pattern theorem in financial time-series is one of the most important technical analysis methods in financial prediction. Recent researches have achieved big progresses in identifying and recognizing time-series patterns. And most of the recent works on time-series deal with this task by using static approaches and mainly focus on the recognition accuracy, but considering that recognition of patterns in financial time-series, especially for stock time-series, are always time-consuming rather than pattern recognition in other fields, a dynamic recognition approach is more preferable so that investment on stock pattern become executable. In this paper we propose a dynamic approach for extracting and recognizing the patterns in stock-series. In our approach a slide window with flexible length is defined for extracting feature vertexes in stock-series, and in addition, a dynamic perceptual important point (PIP) locating method is proposed based on the PIP locating method for avoiding the computation expense problem and an artificial neural network (ANN) approach is involved for pattern recognition and window length identification. |
| Starting Page | 1552 |
| Ending Page | 1555 |
| File Size | 246073 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424450534 |
| DOI | 10.1109/NABIC.2009.5393674 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-12-09 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Production systems Shape Time series analysis Artificial neural networks Pattern recognition Stock series Finacial time-series Neural networks Investments Feature extraction Dynamic approach Pattern analysis |
| Content Type | Text |
| Resource Type | Article |
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