Forecasting Exchange Rate with EMD-Based Support Vector Regression

Fu, Chuanrui

doi:10.1109/icmss.2010.5576363

Cited by 13 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EMD-based model performed significantly better than the SVR model on its own for both singe-step-ahead and multi-step-ahead predictions (W. Wang et al, 2009). Several studies focused on forecasting exchange rates using EMDbased SVR models (Fu 2010;C. Lin et al, 2012;Cheng and Wei 2014).…”

Section: Literature Surveymentioning

confidence: 99%

Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

Mouton¹,

Hoffman

2014

Proceedings of the International Conference on Neural Computation Theory and Applications

View full text Add to dashboard Cite

This paper proposes a neural network based model applied to empirical mode decomposition (EMD) filtered data for multi-step-ahead prediction of exchange rates. EMD is used to decompose the returns of exchange rates into intrinsic mode functions (IMFs) which are partially recomposed to produce a low-pass filtered time series. This series is used to train a neural network for multi-step-ahead prediction. Out-of-sample tests on EUR/USD and USD/JPY rates show superior performance compared to random walk and neural network models that do not employ EMD filtering. The novel approach of using EMD as a filtering technique in combination with neural networks consistently delivers higher returns on investment and demonstrates its utility in multi-step-ahead prediction. Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

Mouton¹,

Hoffman

2014

Proceedings of the International Conference on Neural Computation Theory and Applications

View full text Add to dashboard Cite

show abstract

“…Exchange rate is an essential economic variable, and its movements can make significant impacts on the trading relationships between different economies, international capital flows and interest rates [1]. It is also one of the most important financial markets for investors.…”

Section: Introductionmentioning

confidence: 99%

The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates

Ortiz¹

2016

JCP

View full text Add to dashboard Cite

Abstract:Exchange rates forecasting is a crucial and challenging task. Accurate forecasting of the imminent movements of exchange rates is very important in investments, trade and economics. In this paper, an exponential smoothing using the Holt-Winters Model is used for forecasting exchange rates. Parameter search for the smoothing constants is done through computer simulation using Particle Swarm Optimization (PSO). Experiment results show that PSO is able to compute good values for the smoothing constants, producing forecasts with accuracy in determining the direction (rise or fall) of exchange rates. Furthermore, the computed Mean Absolute Deviation (MAD) and the Residual Standard Error (RSE) of the exchange rate forecasts from the actual observed data indicate that PSO can also be used to improve forecasting precision.

show abstract

“…The summation over all SVR models produces the final forecast value of the method. Specifically in exchange rate forecasting, Fu (2010) applies the EMD-SVR methodology for the weekly EUR/RMB exchange rate and a one-period-ahead forecasting window, producing forecasts for the period 25 June 2005 to 26 June 2009. He concludes that the combined EMD-SVR model forecasts better than the simple SVR model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

Plakandaras

Papadimitriou

Gogas

2015

Journal of Forecasting

View full text Add to dashboard Cite

In this paper we propose and test a forecasting model on monthly and daily spot prices of five selected exchange rates. In doing so, we combine a novel smoothing technique (initially applied in signal processing) with a variable selection methodology and two regression estimation methodologies from the field of machine learning (ML). After the decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method into a smoothed and a fluctuation component, multivariate adaptive regression splines (MARS) are used to select the most appropriate variable set from a large set of explanatory variables that we collected. The selected variables are then fed into two distinctive support vector machines (SVR) models that produce one‐period‐ahead forecasts for the two components. Neural networks (NN) are also considered as an alternative to SVR. The sum of the two forecast components is the final forecast of the proposed scheme. We show that the above implementation exhibits a superior in‐sample and out‐of‐sample forecasting ability when compared to alternative forecasting models. The empirical results provide evidence against the efficient market hypothesis for the selected foreign exchange markets. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

Forecasting Exchange Rate with EMD-Based Support Vector Regression

Cited by 13 publications

References 9 publications

Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates

Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

Contact Info

Product

Resources

About