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Soft Computing

, Volume 23, Issue 10, pp 3545–3561 | Cite as

Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis

  • Jui-Sheng ChouEmail author
  • Thi Thu Ha Truong
Methodologies & Application
  • 42 Downloads

Abstract

The forecasting of exchange rates has become a challenging area of research that has attracted many researchers over recent years. This work presents a sliding-window metaheuristic optimization-based forecast (SMOF) system for one-step ahead forecasting. The proposed system is a graphical user interface, which is developed in the MATLAB environment and functions as a stand-alone application. The system integrates the novel firefly algorithm (FA), metaheuristic (Meta) intelligence, and least squares support vector regression (LSSVR), namely MetaFA-LSSVR, with a sliding-window approach. The MetaFA automatically tunes the hyperparameters of the LSSVR to construct an optimal sliding-window LSSVR prediction model. The optimization effectiveness of the MetaFA is verified using ten benchmark functions. Two case studies on the daily Canadian dollar-USD exchange rate (CAN/USD) and the 4-h closing EUR-USD rates (EUR/USD) were used to confirm the performance of the system, in which the mean absolute percentage errors are 0.2532% and 0.169%, respectively. The forecast system has an 89.8–99.7% greater predictive accuracy than prior work when applied to the currency pair CAN/USD. With respect to the EUR/USD exchange rate, the error rates obtained using the proposed system were 20.8–23.9% better than those obtained by the baseline sliding-window LSSVR model. Therefore, the SMOF system is potentially useful for decision-makers in financial markets.

Keywords

Time series forecasting Exchange rate Metaheuristic computation Optimized machine learning-based system Hybrid soft computing 

Notes

Funding

This study was funded by Ministry of Science and Technology, Taiwan, under the grant number: 107-2221-E-011-035-MY3.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.The University of Danang - University of Technology and EducationDa NangVietnam

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