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 alternatives. Among the linear methods, we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feedforward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of ML in economics and finance and provide an illustration with high-frequency financial data.

中文翻译：

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时间序列预测的机器学习进步

在本文中，我们调查了监督机器学习 (ML) 和用于时间序列预测的高维模型的最新进展。我们同时考虑线性和非线性替代方案。在线性方法中，我们特别关注惩罚回归和模型集成。本文考虑的非线性方法包括前馈和循环版本的浅层和深层神经网络，以及随机森林和提升树等基于树的方法。我们还通过组合来自不同替代品的成分来考虑集成和混合模型。简要回顾了卓越预测能力的测试。最后，我们讨论了 ML 在经济和金融中的应用，并提供了高频金融数据的说明。