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 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Open Access Research Article

Empirical analysis of machine learning techniques for prediction of indian exchange rate

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pp. 13–22Vol. 26Issue 1December 2022DOI: 10.47974/JSMS-943XML
Published Online:
31 Dec 2022
Article type:
Research Article
Language:
EN
Article no.:
JSMS-943
Pages:
13–22

Abstract

Throughout the past few decades, there has been a dramatic surge in the currency market. The adjustments show a vital role in balancing the market’s characteristics. As a result, accurate change price forecasting is essential to improve the success rate of many businesses and fund managers. Despite the fact that the market is renowned for its erratic behaviour and volatility, there are organizations like Agencies, Banks, and others. In order to estimate the extraneous interchange rate of the dollar against the rupee by a high degree of accurateness, we used three distinct types of methodologies in this article. This research uses three different types of neural network models: ANNs (Artificial Neural Networks), LSTMs (Long Short-Term Memory Networks), and GRUs (Gated Recurring Units). The results depict that GRUs model is out performing the other two models.

Keywords

Subject Classifications

68T07

References

[1] Trilok Nath Pandey, Alok Kumar Jagdev, Suman Kumar Mohapatra and Satchidananda Dehuri, “Credit risk analysis using machine learning classifiers,” 2017 International conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1850-1854, 2017.
[2] Trilok Nath Pandey, Alok Kumar Jagadev, Satchidananda Dehuri and Sung-Bae Cho, “A novel committee machine and reviews of neural network and statistical models for currency exchange rate prediction: An experiment analysis” Journal of King Saud University-Computer and Information Sciences, Elsevier, 2018.
[3] Rakhi Mahanta, Trilok Nath Pandey, Alok Kumar Jagadev and Satchidananda Dehuri, “Optimized radial basis functional neural network for stock index prediction” International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 1252-1257, 2016.
[4] Halit Apaydin, Hajar Feizi, Mohammad Taghi Sattari, Muslume Sevba Colak, Shahaboddin Shamshirband and Kwok-Wing Chau, 2022, Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting. Water, 12(5). Link: https://doi.org/10.3390/w12051500.
[5] Trilok Nath Pandey, Tanu Priya, Sanjay Kumar Jena, “ Prediction of Exchange rate in a Cloud computing Enviornment Uising Machine Learning Tools” Intelligent and Cloud Computing, pp. 137-146, 2021.
[6] W. C. Jhee and J. K. Lee, “Performance of Neural Networks in Managerial Forecasting,” Intelligent Systems in Accounting, Finance and Management, vol. 2, pp 55-71, 1993.
[7] L. Cao and f. Tay, “Financial Forecasting Using Support Vector Machines.” Neural Comput & Applic, vol. 10, pp. 184-192, 2001.
[8] I. Kaastra and M. Boyd, “Designing a Neural Network for Forecasting Financial and Economic Time-Series,” Neurocomputing, vol. 10, pp215-236, 1996.
[9] J. Yao and C.L. Tan, “A case study on using neural networks to perform technical forecasting of forex,” Neurocomputing, vol. 34, pp. 79-98, 2000.
[10] K.K. Lai, L. Yu, W. Huang, S.Y. Wang, Multistage neural network metalearning with application to foreign exchange rates forecasting Proceedings of MICAI2006, Lecture Notes in Artificial Intelligence, vol. 4293, Springer, Berlin (2006), pp. 338-347
[11] L.K. Hansen, P. Salamon Neural network ensembles IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), pp. 993-1001
[12] L. Yu, S.Y. Wang, K.K. Lai Foreign-Exchange-Rate Forecasting With Artificial Neural Networks Springer, New York (2007)
[13] K.K. Lai, L. Yu, S.Y. Wang, C.X. Zhou Neural-network-based metamodeling for financial time series forecasting Proceedings of the 9th Joint Conference on Information Sciences, JCIS 2006, Atlantis Press, Paris (2006), pp. 172-175
[14] L. Yu, S.Y. Wang, K.K. Lai A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates Computers & Operations Research, 32 (10) (2005), pp. 2523-2541
[15] Cheng et al.,2006 C.B. Cheng, C.L. Chen, C. Jenfu Financial distress prediction by a radial basis function network with logit analysis learning Comput. Math. Appl., 51 (2006), pp. 579-58
[16] Pandey, T.N., Mahakud, R.R., Patra, B., Giri, P.K., Dehuri, S. (2022). Performance of Machine Learning Techniques Before and After COVID-19 on Indian Foreign Exchange Rate. In: Dehuri, S., Prasad Mishra, B.S., Mallick, P.K., Cho, SB. (eds) Biologically Inspired Techniques in Many Criteria Decision Making. Smart Innovation, Systems and Technologies, vol 271. Springer, Singapore. https://doi.org/10.1007/978-981-16-8739-6_41
[17] Santosini Bhutia, Bichitrananda Patra & Mitrabinda Ray (2022) A hybrid approach for cancer classification based on squirrel search, Journal of Information and Optimization Sciences, 43:5, 905-914, DOI: 10.1080/02522667.2022.2091095.
[18] Hongjoong Kim, Sookyung Jun & Kyoung-Sook Moon (2022) Stock market prediction based on adaptive training algorithm in machine learning, Quantitative Finance, 22:6, 1133- 1152, DOI: 10.1080/14697688.2022.2041208.
[19] Winky K.O. Ho, Bo-Sin Tang & Siu Wai Wong (2021) Predicting property prices with machine learning algorithms, Journal of Property Research, 38:1, 48-70, DOI: 10.1080/09599916.2020.1832558.
[20] Hryshko & T. Downs (2004) System for foreign exchange trading using genetic algorithms and reinforcement learning, International Journal of Systems Science, 35:13-14, 763- 774, DOI: 10.1080/00207720412331303697.

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