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Forecasting daily and monthly exchange rates with machine learning techniques

Theophilos Papadimitriou (), Periklis Gogas () and Vasilios Plakandaras ()

No 3-2013, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics

Abstract: We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States Dollar (USD), USD/Japanese Yen (JPY), Australian Dollar (AUD)/Norwegian Krone (NOK), New Zealand Dollar (NZD)/Brazilian Real (BRL) and South African Rand (ZAR)/Philippine Peso (PHP) exchange rates. After the decomposition with EEMD of the original exchange rate series into a smoothed and a fluctuation component, MARS selects the most informative input datasets from the plethora of variables included in our initial data set. The selected variables are fed into two distinctive SVR models for forecasting each component separately one period ahead for daily and monthly data. The summation of the two forecasted components provides exchange rate forecasts. The above implementation exhibits superior forecasting ability in exchange rate forecasting compared to various models. Overall the proposed model a) is a combination of empirically proven effective techniques in forecasting time series, b) is data driven, c) relies on minimum initial assumptions and d) provides a structural aspect of the forecasting problem.

Keywords: Exchange rate forecasting; Support Vector Regression; local learning; feature selection; Ensemble Empirical Mode Decomposition; time series; trend (search for similar items in EconPapers)
JEL-codes: G15 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eec, nep-for and nep-ore
Date: 2013-03-19, Revised 2015-04-07
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Journal Article: Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques (2015) Downloads
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