A Hybrid-SFLA-Based Hyper-Parameter Tuning Algorithm in SVR for Exchange Rate Prediction
Authors: 
Tanmoy Halder, Souvik Ganguly, Somnath Mukhopadhyay, Tamal Dutta ChaudhuriAuthors Info & Claims
Abstract
This paper proposes a hybrid machine learning-based approach to forecast the exchange rate between the Indian Rupee and the US dollar. The prediction of the exchange rate is carried out by support vector regression (SVR) and a hybrid-shuffled frog leaping algorithm (HSFLA). SVR predicts the exchange rate, whereas the hybrid-SFLA is used to tune the hyper-parameters. Hybrid-SFLA is an improvement over SFLA where random movement of particles is carried out using levy flight distribution. The proposed work has been compared with state-of-the-art hybrid prediction models that have applied different meta-heuristic algorithms. To measure predictive efficiency, root mean square error, mean absolute percentage error, Theil’s U, and average relative variance have been used, and the proposed HSFLA outperformed the other methods.
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© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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Publication History
Published: 24 February 2024
Accepted: 30 December 2023
Received: 30 June 2023
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