Sliding-Window Technique for Enhancing Prediction of Forex Rates
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-30474-3_18
Reference13 articles.
1. Foreign exchange turnover in April 2019. https://www.bis.org/statistics/rpfx19_fx.htm. Accessed 20 Jan 2020
2. Chantarakasemchit, O., Nuchitprasitchai, S., Nilsiam, Y.: Forex rates prediction on EUR/USD with simple moving average technique and financial factors. In: 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 771–774. IEEE (2020)
3. Chantarakasemchit, O., Nuchitprasitchai, S.: Enhancing forex rates prediction with machine learning on EUR to USD with moving average methods and financial factors. In: Recent Advances in Information and Communication Technology on Proceedings of the 17th International Conference on Computing and Information Technology (IC2IT 2021), pp. 44–54. Cham: Springer International Publishing (2021)
4. Braverman, V., Ostrovsky, R., Zaniolo, C.: Optimal sampling from sliding windows. In: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 147–156. (2009)
5. Chou JS, Truong TTH (2019) Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis. Soft Comput. 23(10):3545–3561
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