Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems ….g. a hybrid of a random forest and lasso). We illustrate the application of the general theory through application to the …

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly …

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time … series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to … predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide …

algorithm based on natural language processing and deep learning techniques for the quantification of economic policy …, financial forecasting, and potentially, derivative pricing. …

of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models … shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al … parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine …