The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
M5 accuracy competition: Results, findings, and conclusions
Open accessAbstract
In this study, we present the results of the M5 “Accuracy” competition, which was the first of two parallel challenges in the latest M competition with the aim of advancing the theory and practice of forecasting. The main objective in the M5 “Accuracy” competition was to accurately predict 42,840 time series representing the hierarchical unit sales for the largest retail company in the world by revenue, Walmart. The competition required the submission of 30,490 point forecasts for the lowest cross-sectional aggregation level of the data, which could then be summed up accordingly to estimate forecasts for the remaining upward levels. We provide details of the implementation of the M5 “Accuracy” challenge, as well as the results and best performing methods, and summarize the major findings and conclusions. Finally, we discuss the implications of these findings and suggest directions for future research.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881
Another look at measures of forecast accuracy
Hyndman, R. J., & Koehler, A. B. (2006) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2006.03.001 · Cited by 5,757
Forecasting with artificial neural networks:
Zhang, G., Eddy Patuwo, B., & Y. Hu, M. (1998) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00044-7 · Cited by 4,229
A scaled conjugate gradient algorithm for fast supervised learning
Møller, M. F. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80056-5 · Cited by 4,052
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., & Januschowski, T. (2020) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2019.07.001 · Cited by 2,661
The M3-Competition: results, conclusions and implications
Makridakis, S., & Hibon, M. (2000) · International Journal of Forecasting · doi:10.1016/s0169-2070(00)00057-1 · Cited by 1,695
Self-citationStatistical and machine learning forecasting methods: Concerns and ways forward
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · PLOS ONE · doi:10.1371/journal.pone.0194889 · Cited by 1,528
Self-citationThe accuracy of extrapolation (time series) methods: Results of a forecasting competition
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E., & Winkler, R. (1982) · Journal of Forecasting · doi:10.1002/for.3980010202 · Cited by 1,462
Self-citationExponential smoothing: The state of the art
Gardner, E. S. (1985) · Journal of Forecasting · doi:10.1002/for.3980040103 · Cited by 1,458
Cited by
Forecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 280
Metadata
- Authors
- Makridakis, S., Spiliotis, E., & Assimakopoulos, V.
- Venue
- International Journal of Forecasting
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 38
- Issue
- 4
- Pages
- 1346-1364
- ISSN
- 0169-2070 (ISSN-L)
- OpenAlex
- W4206173445
- Cited by
- 554
- References
- 56
- FWCI
- 54.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: M5 accuracy competition: Results, findings, and conclusions. https://app.cochat.ai/papers/10.1016/j.ijforecast.2021.11.013
- The paper
- Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2022). M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting. https://doi.org/10.1016/j.ijforecast.2021.11.013
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
