Comparative Analysis Based on Machine Learning Model Predicting Exchange Rate Fluctuations

DOI

10.2991/978-94-6463-542-3_55How to use a DOI?

Keywords

Machine learning; exchange rate; LSTM Model; Random Forest Model; Linear Regression Model

Abstract

The foreign exchange market has long been a focal point in the financial sector, attracting considerable attention. On a macro level, accurately predicting exchange rate trends is crucial for shaping effective economic and financial policies. For micro-entities, effectively managing and mitigating foreign exchange risks is a key challenge. Thus, forecasting foreign exchange trends holds significant importance across various fields. This article will use the method of model comparison to bring actual data to analyze the three models in the field of foreign exchange forecasting. The three different machine learning models, namely the Linear Regression Model (LSTM), Random Forest Model, and Long Short-term Memory Model, are widely used in the field of artificial intelligence, to predict the foreign exchange rate in a certain period. And choose the best foreign exchange prediction model while comparing their respective advantages. From the perspective of model prediction performance on the same set of foreign exchange data, the LSTM model became the optimal prediction model with the smallest mean square error (2.54e-05) among the three models.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ziqian Niu
PY  - 2024
DA  - 2024/10/15
TI  - Comparative Analysis Based on Machine Learning Model Predicting Exchange Rate Fluctuations
BT  - Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024)
PB  - Atlantis Press
SP  - 471
EP  - 480
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-542-3_55
DO  - 10.2991/978-94-6463-542-3_55
ID  - Niu2024
ER  -