# Forecasting: theory and practice

arXiv: https://arxiv.org/abs/2012.03854 · HTML: https://mlsanity.com/paper/2012.03854/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 30 of 36)

- [Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices](https://mlsanity.com/paper/2203.10716/) (2022)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Distributional neural networks for electricity price forecasting](https://mlsanity.com/paper/2207.02832/) (2022)
- [Generative learning for nonlinear dynamics](https://mlsanity.com/paper/2311.04128/) (2023)
- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics](https://mlsanity.com/paper/2206.10696/) (2022)
- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting](https://mlsanity.com/paper/2209.03945/) (2022)
- [Counterfactual Explanations for Time Series Forecasting](https://mlsanity.com/paper/2310.08137/) (2023)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [A feature selection method based on Shapley values robust to concept shift in regression](https://mlsanity.com/paper/2304.14774/) (2023)
- [Aggregating distribution forecasts from deep ensembles](https://mlsanity.com/paper/2204.02291/) (2022)
- [Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction](https://mlsanity.com/paper/2304.04697/) (2023)
- [A Joint Time-frequency Domain Transformer for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2305.14649/) (2023)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)
- [Using dynamic loss weighting to boost improvements in forecast stability](https://mlsanity.com/paper/2409.18267/) (2024)
- [Expert Aggregation for Financial Forecasting](https://mlsanity.com/paper/2111.15365/) (2021)
- [Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2312.06874/) (2023)
- [Stratify: Unifying Multi-Step Forecasting Strategies](https://mlsanity.com/paper/2412.20510/) (2024)
- [On the retraining frequency of global models in retail demand forecasting](https://mlsanity.com/paper/2505.00356/) (2025)
- [Leveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets](https://mlsanity.com/paper/2507.13250/) (2025)
- [A Natural Gas Consumption Forecasting System for Continual Learning Scenarios based on Hoeffding Trees with Change Point Detection Mechanism](https://mlsanity.com/paper/2309.03720/) (2023)
- [Efficient mid-term forecasting of hourly electricity load using generalized additive models](https://mlsanity.com/paper/2405.17070/) (2024)
- [A Hybrid Strategy for Probabilistic Forecasting and Trading of Aggregated Wind-Solar Power: Design and Analysis in HEFTCom2024](https://mlsanity.com/paper/2505.10367/) (2025)
- [Explainable time-series forecasting with sampling-free SHAP for Transformers](https://mlsanity.com/paper/2512.20514/) (2025)
- [A Hybrid Framework for Sequential Data Prediction with End-to-End Optimization](https://mlsanity.com/paper/2203.13787/) (2022)
- [Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions](https://mlsanity.com/paper/2305.16255/) (2023)
- [Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure](https://mlsanity.com/paper/2308.03472/) (2023)
- [GeOT: A spatially explicit framework for evaluating spatio-temporal predictions](https://mlsanity.com/paper/2410.11709/) (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)

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.