mlsanityData to 7 Oct 2026

Criteria for Classifying Forecasting Methods

arXiv 2212.03523 ↗First posted 7 Dec 2022stat.ML cs.LGcited by 48

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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.

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

Builds on

28 of 103 references.

References · 28

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2013Auto-Encoding Variational Bayescited by 3,980
  4. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  5. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  6. 2016Understanding deep learning requires rethinking generalizationcited by 1,657
  7. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  8. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  9. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
  10. 2015Weight Uncertainty in Neural Networkscited by 554
  11. 2018Pyro: Deep Universal Probabilistic Programmingcited by 379
  12. 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
16 more
  1. 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
  2. 2016Generalized Random Forestscited by 309
  3. 2014The Loss Surfaces of Multilayer Networkscited by 278
  4. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  5. 2015MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systemscited by 195
  6. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  7. 2015Deep Kalman Filterscited by 163
  8. 2016Sequential Neural Models with Stochastic Layerscited by 115
  9. 2017A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learningcited by 107
  10. 2019Deep Factors for Forecastingcited by 86
  11. 2018Deep Neural Networks for Estimation and Inferencecited by 62
  12. 2013Regularized estimation in sparse high-dimensional time series modelscited by 40
  13. 2016Capacity and Trainability in Recurrent Neural Networkscited by 26
  14. 2018Deep Factors with Gaussian Processes for Forecastingcited by 16
  15. 2013Oracle Inequalities for High Dimensional Vector Autoregressionscited by 11
  16. 2018ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecastingcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 14; 2024: 11; 2025: 12; 2026: 10.

48 papers.

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  1. 2023TimeGPT-1cited by 149
  2. 2023Taming Local Effects in Graph-based Spatiotemporal Forecastingcited by 37
  3. 2023AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecastingcited by 36
  4. 2023Graph Deep Learning for Time Series Forecastingcited by 26
  5. 2023pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecastingcited by 16
  6. 2023Deep Learning based Forecasting: a case study from the online fashion industrycited by 12
  7. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  8. 2026Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecastingcited by 8
  9. 2023Deep Non-Parametric Time Series Forecastercited by 5
  10. 2022Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecastingcited by 4
  11. 2023Handling Concept Drift in Global Time Series Forecastingcited by 4
  12. 2023Scalable Probabilistic Forecasting in Retail with Gradient Boosted Trees: A Practitioner's Approachcited by 4
36 more
  1. 2024System Safety Monitoring of Learned Components Using Temporal Metric Forecastingcited by 4
  2. 2024Using dynamic loss weighting to boost improvements in forecast stabilitycited by 4
  3. 2025On the retraining frequency of global models in retail demand forecastingcited by 3
  4. 2023MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecastingcited by 2
  5. 2023Local and Global Trend Bayesian Exponential Smoothing Modelscited by 2
  6. 2023TSPP: A Unified Benchmarking Tool for Time-series Forecastingcited by 2
  7. 2024Time Series Data Augmentation as an Imbalanced Learning Problemcited by 2
  8. 2024Lag Selection for Univariate Time Series Forecasting using Deep Learning: An Empirical Studycited by 2
  9. 2025ModelRadar: Aspect-based Forecast Evaluationcited by 2
  10. 2025Tsururu: A Python-based Time Series Forecasting Strategies Librarycited by 2
  11. 2025Test-Time Efficient Pretrained Model Portfolios for Time Series Forecastingcited by 2
  12. 2023Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive modelscited by 1
  13. 2023Causal Forecasting for Pricingcited by 1
  14. 2024Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning modelscited by 1
  15. 2024Forecasting with Hyper-Treescited by 1
  16. 2024Forecasting with Deep Learning: Beyond Average of Average of Average Performancecited by 1
  17. 2024On the Regularization of Learnable Embeddings for Time Series Forecastingcited by 1
  18. 2024Local vs. Global Models for Hierarchical Forecastingcited by 1
  19. 2025Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resiliencecited by 1
  20. 2025The cost of ensembling: is it always worth combining?cited by 1
  21. 2025N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecastingcited by 1
  22. 2026Intermittent time series forecasting: local vs global modelscited by 1
  23. 2026Selective Time Series Forecasting via Metalearningcited by 1
  24. 2024Meta-learning and Data Augmentation for Stress Testing Forecasting Modelscited by 0
  25. 2024Designing Time-Series Models With Hypernetworks & Adversarial Portfolioscited by 0
  26. 2025Forecast-Then-Optimize Deep Learning Methodscited by 0
  27. 2025Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecturecited by 0
  28. 2025Multi-layer Stack Ensembles for Time Series Forecastingcited by 0
  29. 2025Position: Current Benchmarking Hinders Real Progress in Deep Learning for Time Series Forecastingcited by 0
  30. 2026One Global Model, Many Behaviors: Stockout-Aware Feature Engineering and Dynamic Scaling for Multi-Horizon Retail Demand Forecasting with a Cost-Aware Ordering Policy (VN2 Winner Report)cited by 0
  31. 2026Stabilizing distribution-free probabilistic forecastscited by 0
  32. 2026Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid loadcited by 0
  33. 2026Benchmarking Time Series Generation Methods for Privacy-Preserving Forecastingcited by 0
  34. 2026Global tree forecasters collapse at the hierarchical aggregate: a five-panel failure characterizationcited by 0
  35. 2026fable.intermittent: benchmarking probabilistic forecasting methods for intermittent time seriescited by 0
  36. 2026The Nixtlaverse: An Open-Source Ecosystem for Forecastingcited by 0