In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

This paper studies the advances in time series forecasting models using artificial neural
network methodologies in a systematic literature review. The systematic review has been …

We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

Summary We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by
adding the how. The current forecasting literature has focused on matching specific …

In this work we present a large scale comparison study for the major machine learning
models for time series forecasting. Specifically, we apply the models on the monthly M3 time …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

Multistep-ahead forecasts can either be produced recursively by iterating a one-step-ahead
time series model or directly by estimating a separate model for each forecast horizon. In …

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition
autoregressive (STAR), and neural network (NN) time series models for 47 monthly …

Modelling artificial neural networks for accurate time series prediction poses multiple
challenges, in particular specifying the network architecture in accordance with the …

The goal of this paper is to describe a forecasting model for the hourly electricity load in the
area covered by an electric utility located in the southeast of Brazil. A different model is …