Name
Abstract | ||
|---|---|---|
Making use of the function approximation and self-learning of BP neural network, we analyze the historical data in Shanghai Stock between June 2006 and November 2009, construct a stock forecasting model based on BP neural network, and verify the model through some test samples. Finally, we can use the Robust model to forcast the short-term stock. Matlab simulation experiments indicate that the model is feasible and effective in short-term stock forcasting. © 2011 IEEE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/EMEIT.2011.6023120 | EMEIT |
Keywords | Field | DocType |
bp neural network,function approximability,stock forcasting,social development,neural network,backpropagation,neural nets,economic forecasting,function approximation,simulation experiment | Economic forecasting,Function approximation,Stock forecasting,Artificial intelligence,Engineering,Artificial neural network,Backpropagation,Applied research,Machine learning,Matlab simulation | Conference |
Volume | Issue | Citations |
9 | null | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yue Ma | 1 | 32 | 8.42 |
| Yu Chang | 2 | 8 | 1.48 |
| Chunyu Xia | 3 | 0 | 0.34 |