NDLI: Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms

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Financial information processing

Stock prediction: an event-driven approach based on bursty keywords

Risk aversion and agents’ survivability in a financial market

Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms

Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics

A class of classification and regression methods by multiobjective programming

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Forecasting foreign exchange rates with an improved back-propagation learning algorithm with adaptive smoothing momentum terms

Content Provider	Springer Nature Link
Author	Yu, Lean Wang, Shouyang Lai, Kin Keung
Copyright Year	2009
Abstract	The slow convergence of back-propagation neural network (BPNN) has become a challenge in data-mining and knowledge discovery applications due to the drawbacks of the gradient descent (GD) optimization method, which is widely adopted in BPNN learning. To solve this problem, some standard optimization techniques such as conjugate-gradient and Newton method have been proposed to improve the convergence rate of BP learning algorithm. This paper presents a heuristic method that adds an adaptive smoothing momentum term to original BP learning algorithm to speedup the convergence. In this improved BP learning algorithm, adaptive smoothing technique is used to adjust the momentums of weight updating formula automatically in terms of “3 σ limits theory.” Using the adaptive smoothing momentum terms, the improved BP learning algorithm can make the network training and convergence process faster, and the network’s generalization performance stronger than the standard BP learning algorithm can do. In order to verify the effectiveness of the proposed BP learning algorithm, three typical foreign exchange rates, British pound (GBP), Euro (EUR), and Japanese yen (JPY), are chosen as the forecasting targets for illustration purpose. Experimental results from homogeneous algorithm comparisons reveal that the proposed BP learning algorithm outperforms the other comparable BP algorithms in performance and convergence rate. Furthermore, empirical results from heterogeneous model comparisons also show the effectiveness of the proposed BP learning algorithm.
Starting Page	167
Ending Page	176
Page Count	10
File Format	PDF
ISSN	16737350
Journal	Frontiers of Computer Science in China
Volume Number	3
Issue Number	2
e-ISSN	16737466
Language	English
Publisher	SP Higher Education Press
Publisher Date	2009-05-08
Publisher Institution	Chinese Universities
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	back-propagation neural network adaptive smoothing momentum heuristic method foreign exchange rates forecasting Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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