# Machine Learning Advances for Time Series Forecasting

arXiv: https://arxiv.org/abs/2012.12802 · HTML: https://mlsanity.com/paper/2012.12802/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2020.

## Built on by (most cited first, 30 of 49)

- [Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processes](https://mlsanity.com/paper/2403.13724/) (2024)
- [Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts](https://mlsanity.com/paper/2210.03675/) (2022)
- [STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction](https://mlsanity.com/paper/2312.17346/) (2023)
- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Feature Programming for Multivariate Time Series Prediction](https://mlsanity.com/paper/2306.06252/) (2023)
- [Assessing the Impact of Distribution Shift on Reinforcement Learning Performance](https://mlsanity.com/paper/2402.03590/) (2024)
- [DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions](https://mlsanity.com/paper/2210.04797/) (2022)
- [The Age of Correlated Features in Supervised Learning based Forecasting](https://mlsanity.com/paper/2103.00092/) (2021)
- [Differential Convolutional Fuzzy Time Series Forecasting](https://mlsanity.com/paper/2305.08890/) (2023)
- [The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Tasks](https://mlsanity.com/paper/2506.08274/) (2025)
- [Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators](https://mlsanity.com/paper/2209.10705/) (2022)
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://mlsanity.com/paper/2308.00709/) (2023)
- [Conditional Denoising Meets Polynomial Modeling: A Flexible Decoupled Framework for Time Series Forecasting](https://mlsanity.com/paper/2410.13253/) (2024)
- [Stabilizing black-box model selection with the inflated argmax](https://mlsanity.com/paper/2410.18268/) (2024)
- [Stratify: Unifying Multi-Step Forecasting Strategies](https://mlsanity.com/paper/2412.20510/) (2024)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Multivariate Time Series characterization and forecasting of VoIP traffic in real mobile networks](https://mlsanity.com/paper/2307.06645/) (2023)
- [Learning Low-dimensional Latent Dynamics from High-dimensional Observations: Non-asymptotics and Lower Bounds](https://mlsanity.com/paper/2405.06089/) (2024)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [From Causal Discovery to Dynamic Causal Inference in Neural Time Series](https://mlsanity.com/paper/2603.20980/) (2026)
- [A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting](https://mlsanity.com/paper/2207.14219/) (2022)
- [Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting](https://mlsanity.com/paper/2212.07771/) (2022)
- [Concentration inequalities for high-dimensional linear processes with dependent innovations](https://mlsanity.com/paper/2307.12395/) (2023)
- [A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades](https://mlsanity.com/paper/2308.00855/) (2023)
- [Econometrics of Machine Learning Methods in Economic Forecasting](https://mlsanity.com/paper/2308.10993/) (2023)
- [auto-sktime: Automated Time Series Forecasting](https://mlsanity.com/paper/2312.08528/) (2023)
- [Machine learning and economic forecasting: the role of international trade networks](https://mlsanity.com/paper/2404.08712/) (2024)
- [Predicting Company Growth using Scaling Theory informed Machine Learning](https://mlsanity.com/paper/2410.17587/) (2024)
- [Time-Series Forecasting in Smart Manufacturing Systems: An Experimental Evaluation of the State-of-the-art Algorithms](https://mlsanity.com/paper/2411.17499/) (2024)

## Abstract (arXiv)

In this paper we survey the most recent advances in supervised machine learning 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 feed-forward 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 machine learning in economics and finance and provide an illustration with high-frequency financial data.