Nonparametric prediction of time series is a viable alternative to parametric prediction, since parametric prediction relies on the correct specification of the process, its order and the distribution of the innovations. Often these are not known and have to be estimated from the data. Another...

In this paper, we present a novel machine learning based forecasting system of the EUR/USD exchange rate directional changes. Specifically, we feed an overcomplete variable set to a Support Vector Machines (SVM) model and refine it through a Sensitivity Analysis process. The dataset spans from...

We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the time series decomposed with EMD. The outcomes...