@inbook{b680760dbd644640bf5fb936484487b8,
title = "Nonlinear Forecasting of Energy Futures",
abstract = "This paper proposes the use of the Brownian distance correlation for feature selection and for conducting a lead-lag analysis of energy time series. Brownian distance correlation determines relationships similar to those identified by the linear Granger causality test, and it also uncovers additional non-linear relationships among the log return of oil, coal, and natural gas. When these linear and non-linear relationships are used to forecast the direction of energy futures log return with a non-linear classification method such as support vector machine, the forecast of energy futures log return improve when compared to a forecast based only on Granger causality.",
keywords = "Artificial agents, Energy finance, Financial forecasting, Lead-lag relationship, Non-linear correlation, Support vector machine",
author = "Creamer, {Germ{\'a}n G.}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer International Publishing AG, part of Springer Nature.",
year = "2019",
doi = "10.1007/978-3-319-77604-0_1",
language = "English",
series = "Studies in Big Data",
pages = "3--14",
booktitle = "Studies in Big Data",
}