Name
Abstract | ||
|---|---|---|
This paper describes a non trivial application in forecasting currency exchange rates, and its implementation using a multi-layer perceptron network. We show that with careful network design, the backpropagation learning procedure is an effective way of training neural networks for time series prediction. The choice of squashing function is an important design issue in achieving fast convergence and good generalisation performance. We evaluate the use of symmetric and asymmetric squashing functions in the learning procedure, and show that symmetric functions yield faster convergence and better generalisation performance. We derive analytic results to show the conditions under which symmetric squashing functions yield faster convergence, and to quantify the upper bounds on the convergence improvement. The network is evaluated both for long-term forecasting without feedback (i.e. only the forecast prices are used for the remaining trading days), and for short- term forecasting with hourly feedback. The network learns the training set near perfect, and shows accurate prediction, making at least 22% profit on the last 60 trading days of 1989. |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1007/BF01411374 | Neural Computing and Applications |
Keywords | DocType | Volume |
time-series prediction,neural network,exchange rate forecasting,backpropagation | Journal | 1 |
Issue | Citations | PageRank |
1 | 43 | 10.27 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Apostolos Nikolaos Refenes | 1 | 62 | 22.94 |
| Magali E. Azema-barac | 2 | 59 | 20.75 |
| L. Chen | 3 | 43 | 10.27 |
| S. A. Karoussos | 4 | 43 | 10.27 |