# Criteria for Classifying Forecasting Methods

arXiv: https://arxiv.org/abs/2212.03523 · HTML: https://mlsanity.com/paper/2212.03523/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 103 references that are in the map.
- First posted on arXiv 7 Dec 2022.

## Builds on (most cited first, 28 of 28)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Weight Uncertainty in Neural Networks](https://mlsanity.com/paper/1505.05424/) (2015)
- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [Unmasking Clever Hans Predictors and Assessing What Machines Really Learn](https://mlsanity.com/paper/1902.10178/) (2019)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [The Loss Surfaces of Multilayer Networks](https://mlsanity.com/paper/1412.0233/) (2014)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems](https://mlsanity.com/paper/1512.01274/) (2015)
- [Structured Inference Networks for Nonlinear State Space Models](https://mlsanity.com/paper/1609.09869/) (2016)
- [Deep Kalman Filters](https://mlsanity.com/paper/1511.05121/) (2015)
- [Sequential Neural Models with Stochastic Layers](https://mlsanity.com/paper/1605.07571/) (2016)
- [A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning](https://mlsanity.com/paper/1710.05741/) (2017)
- [Deep Factors for Forecasting](https://mlsanity.com/paper/1905.12417/) (2019)
- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [Regularized estimation in sparse high-dimensional time series models](https://mlsanity.com/paper/1311.4175/) (2013)
- [Capacity and Trainability in Recurrent Neural Networks](https://mlsanity.com/paper/1611.09913/) (2016)
- [Deep Factors with Gaussian Processes for Forecasting](https://mlsanity.com/paper/1812.00098/) (2018)
- [Oracle Inequalities for High Dimensional Vector Autoregressions](https://mlsanity.com/paper/1311.0811/) (2013)
- [ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting](https://mlsanity.com/paper/1803.03800/) (2018)

## Built on by (most cited first, 30 of 48)

- [TimeGPT-1](https://mlsanity.com/paper/2310.03589/) (2023)
- [Taming Local Effects in Graph-based Spatiotemporal Forecasting](https://mlsanity.com/paper/2302.04071/) (2023)
- [AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2308.05566/) (2023)
- [Graph Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2310.15978/) (2023)
- [pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2305.11304/) (2023)
- [Deep Learning based Forecasting: a case study from the online fashion industry](https://mlsanity.com/paper/2305.14406/) (2023)
- [Deep Learning for Time Series Forecasting: A Survey](https://mlsanity.com/paper/2503.10198/) (2025)
- [Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting](https://mlsanity.com/paper/2602.01776/) (2026)
- [Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2212.09030/) (2022)
- [Deep Non-Parametric Time Series Forecaster](https://mlsanity.com/paper/2312.14657/) (2023)
- [Handling Concept Drift in Global Time Series Forecasting](https://mlsanity.com/paper/2304.01512/) (2023)
- [Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approach](https://mlsanity.com/paper/2311.00993/) (2023)
- [System Safety Monitoring of Learned Components Using Temporal Metric Forecasting](https://mlsanity.com/paper/2405.13254/) (2024)
- [Using dynamic loss weighting to boost improvements in forecast stability](https://mlsanity.com/paper/2409.18267/) (2024)
- [On the retraining frequency of global models in retail demand forecasting](https://mlsanity.com/paper/2505.00356/) (2025)
- [MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting](https://mlsanity.com/paper/2306.10356/) (2023)
- [Local and Global Trend Bayesian Exponential Smoothing Models](https://mlsanity.com/paper/2309.13950/) (2023)
- [Causal Forecasting for Pricing](https://mlsanity.com/paper/2312.15282/) (2023)
- [TSPP: A Unified Benchmarking Tool for Time-series Forecasting](https://mlsanity.com/paper/2312.17100/) (2023)
- [Time Series Data Augmentation as an Imbalanced Learning Problem](https://mlsanity.com/paper/2404.18537/) (2024)
- [Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Study](https://mlsanity.com/paper/2405.11237/) (2024)
- [ModelRadar: Aspect-based Forecast Evaluation](https://mlsanity.com/paper/2504.00059/) (2025)
- [Tsururu: A Python-based Time Series Forecasting Strategies Library](https://mlsanity.com/paper/2509.15843/) (2025)
- [Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting](https://mlsanity.com/paper/2510.06419/) (2025)
- [Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models](https://mlsanity.com/paper/2302.08192/) (2023)
- [Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models](https://mlsanity.com/paper/2404.17884/) (2024)
- [Forecasting with Hyper-Trees](https://mlsanity.com/paper/2405.07836/) (2024)
- [Forecasting with Deep Learning: Beyond Average of Average of Average Performance](https://mlsanity.com/paper/2406.16590/) (2024)
- [On the Regularization of Learnable Embeddings for Time Series Forecasting](https://mlsanity.com/paper/2410.14630/) (2024)
- [Local vs. Global Models for Hierarchical Forecasting](https://mlsanity.com/paper/2411.06394/) (2024)

## Abstract (arXiv)

Classifying forecasting methods as being either of a "machine learning" or "statistical" nature has become commonplace in parts of the forecasting literature and community, as exemplified by the M4 competition and the conclusion drawn by the organizers. We argue that this distinction does not stem from fundamental differences in the methods assigned to either class. Instead, this distinction is probably of a tribal nature, which limits the insights into the appropriateness and effectiveness of different forecasting methods. We provide alternative characteristics of forecasting methods which, in our view, allow to draw meaningful conclusions. Further, we discuss areas of forecasting which could benefit most from cross-pollination between the ML and the statistics communities.