Name
Title | ||
|---|---|---|
Integrating a Piecewise Linear Representation Method with Dynamic Time Warping System for Stock Trading Decision Making |
Abstract | ||
|---|---|---|
Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, a piecewise linear representation method with dynamics time warping system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base, then the dynamic time warping system will be applied to retrieve similar stock price patterns from historic data for training the system. These turning points represent short-term trading signals for selling or buying stocks from the market. A Back-Propagation neural network (B.P.N) is further applied to learn the connection weights from these historic turning points and afterwards it is applied to forecast the future turning points from the set of test data. Experimental results demonstrate that the system integrating PLR and neural networks can make a significant amount of profit when compared with other approaches using stock data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.3 | ICNC |
Keywords | Field | DocType |
time warp simulation,stock price patterns,stock trading decision making,back-propagation neural network,historic data,stock turning points,stock trading decision,decision making,dynamics time warping system,points detection,stock data,piecewise linear representation method,backpropagation,stock markets,numerous stock,test data,dynamic time warping system,historic data base,piecewise linear techniques,stock turning points detection,similar stock price pattern,piecewise linear representation,plr method,neural nets,system integration,artificial neural networks,time series analysis,profitability,neural network,piecewise linear,dynamic time warping,forecasting | Time series,Mathematical optimization,Dynamic time warping,Computer science,Artificial intelligence,Test data,Stock (geology),Backpropagation,Artificial neural network,Piecewise linear representation,Stock trading,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3304-9 | 2 |
PageRank | References | Authors |
0.40 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei-Chann Chang | 1 | 1752 | 109.32 |
| Chin-Yuan Fan | 2 | 473 | 28.27 |
| Jun-lin Lin | 3 | 401 | 51.05 |
| Jyun-Jie Lin | 4 | 150 | 7.31 |