The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Neural network forecasting for seasonal and trend time series
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
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
The Impact of Empirical Accuracy Studies on Time Series Analysis and Forecasting
Fildes, R., & Makridakis, S. (1995) · International Statistical Review / Revue Internationale de Statistique · doi:10.2307/1403481 · Cited by 172
Cited by
Statistical 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
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371
Ben Taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.039 · Cited by 734
Predicting solar radiation at high resolutions: A comparison of time series forecasts
Reikard, G. (2009) · Solar Energy · doi:10.1016/j.solener.2008.08.007 · Cited by 649
Wang, L., Zeng, Y., & Chen, T. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.018 · Cited by 631
Metadata
- Authors
- Zhang, G. P., & Qi, M.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2005-01
- Volume
- 160
- Issue
- 2
- Pages
- 501-514
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2011227258
- Cited by
- 986
- References
- 3
- FWCI
- 17.85
- Type
- journal article
- Language
- en
- Open access
- closed
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: Neural network forecasting for seasonal and trend time series. https://app.cochat.ai/papers/10.1016/j.ejor.2003.08.037
- The paper
- Zhang, G. P., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2003.08.037
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.
