TY - GEN
T1 - Application of neural networks for foreign exchange rates forecasting with noise reduction
AU - Huang, Wei
AU - Lai, Kin Keung
AU - Wang, Shouyang
PY - 2007
Y1 - 2007
N2 - Predictive models are generally fitted directly from the original noisy data. It is well known that noise can seriously limit the prediction performance on time series. In this study, we apply the nonlinear noise reduction methods to the problem of foreign exchange rates forecasting with neural networks (NNs). The experiment results show that the nonlinear noise reduction methods can improve the prediction performance of NNs. Based on the modified DieboldMariano test, the improvement is not statistically significant in most cases. We may need more effective nonlinear noise reduction methods to improve prediction performance further. On the other hand, it indicates that NNs are particularly well appropriate to find underlying relationship in the environment characterized by complex, noisy, irrelevant or partial information. We also find that the nonlinear noise reduction methods work more effectively when the foreign exchange rates are more volatile. © verlag-Bierlin Heidelberg 2007.
AB - Predictive models are generally fitted directly from the original noisy data. It is well known that noise can seriously limit the prediction performance on time series. In this study, we apply the nonlinear noise reduction methods to the problem of foreign exchange rates forecasting with neural networks (NNs). The experiment results show that the nonlinear noise reduction methods can improve the prediction performance of NNs. Based on the modified DieboldMariano test, the improvement is not statistically significant in most cases. We may need more effective nonlinear noise reduction methods to improve prediction performance further. On the other hand, it indicates that NNs are particularly well appropriate to find underlying relationship in the environment characterized by complex, noisy, irrelevant or partial information. We also find that the nonlinear noise reduction methods work more effectively when the foreign exchange rates are more volatile. © verlag-Bierlin Heidelberg 2007.
UR - http://www.scopus.com/inward/record.url?scp=38049103909&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-38049103909&origin=recordpage
U2 - 10.1007/978-3-540-72586-2_65
DO - 10.1007/978-3-540-72586-2_65
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9783540725855
VL - 4488 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 455
EP - 461
BT - Computational Science - ICCS 2007
PB - Springer Verlag
T2 - 7th International Conference on Computational Science (ICCS 2007)
Y2 - 27 May 2007 through 30 May 2007
ER -