Predicting multi-horizon currency exchange rates using a stacked ensemble of random forest and SVR

Abstract

A thorough exploration of the effects of a given minute’s currency exchange rates on subsequent 1, 5, 10, 15, 30, 45, and 60 minutes’ currency exchange rates is presented in this article, with machine learning and ensemble methods being applied. The focus is on twelve currency pairs, including EUR/AUD, EUR/GBP, and EUR/PLN, with a data set of per-minute logs of these pairs’ exchange rates from 2022 being leveraged. A stacked ensemble of Random Forest and Support Vector Regression (SVR) is used to predict future exchange rates. A comparison of this model is also made with the single RF, single SVR, and an average ensemble of RF and SVR models. The research method is further fortified by the use of k-fold cross-validation and ANOVA tests. The findings of the study reveal significant predictive accuracy of the stacked ensemble model, emphasizing the intricate interconnections of currency exchange rates. The potential of machine learning and ensemble techniques in predicting short-term currency exchange rates is underlined, thereby augmenting financial forecasting research.