IEICE TRANSACTIONS on Fundamentals
Performance Evaluation of Two Algorithms for Learning in ANN Based on a Real Financial Prediction
Summary :
The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a proposed modification to the first one by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show not only that NBP performs better than OBP since the former speeds up convergence time to a given error value, but also NBP shows a good generalization performance.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E80-A No.2 pp.407-412
- Publication Date
- 1997/02/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Category
- Neural Networks
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Yadira SOLANO, Hiroaki IKEDA, "Performance Evaluation of Two Algorithms for Learning in ANN Based on a Real Financial Prediction" in IEICE TRANSACTIONS on Fundamentals,
vol. E80-A, no. 2, pp. 407-412, February 1997, doi: .
Abstract: The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a proposed modification to the first one by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show not only that NBP performs better than OBP since the former speeds up convergence time to a given error value, but also NBP shows a good generalization performance.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e80-a_2_407/_p
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@ARTICLE{e80-a_2_407,
author={Yadira SOLANO, Hiroaki IKEDA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Performance Evaluation of Two Algorithms for Learning in ANN Based on a Real Financial Prediction},
year={1997},
volume={E80-A},
number={2},
pages={407-412},
abstract={The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a proposed modification to the first one by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show not only that NBP performs better than OBP since the former speeds up convergence time to a given error value, but also NBP shows a good generalization performance.},
keywords={},
doi={},
ISSN={},
month={February},}
Copy
TY - JOUR
TI - Performance Evaluation of Two Algorithms for Learning in ANN Based on a Real Financial Prediction
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 407
EP - 412
AU - Yadira SOLANO
AU - Hiroaki IKEDA
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E80-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 1997
AB - The purpose of this study is to present results of forecast of ranges for yen to US dollar exchange rate fluctuation in order to evaluate the performance of two algorithms: the original backpropagation (OBP), which is the most widely used algorithm, and the second algorithm (NBP), which is a proposed modification to the first one by the authors. The set of data consisted of economic and financial values that have already been calculated by the Bank of Japan and the Japanese Ministry of Planning and Finance. This data was available though the Nikkei Data Service and stretched from January, 1986, to the end of December, 1992. The results obtained show not only that NBP performs better than OBP since the former speeds up convergence time to a given error value, but also NBP shows a good generalization performance.
ER -
English
