The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Machine Learning Strategies for Time Series Forecasting
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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
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
Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.712 · Cited by 3,869
Beyond regression : new fools for prediction and analysis in the behavioral sciences
Werbos, P. J. (1974) · Medical Entomology and Zoology · Cited by 2,739
Dynamical Systems and Turbulence
Takens, F. (1981) · Medical Entomology and Zoology · Cited by 2,142
The Statistical Analysis of Time Series.
Anderson, T. W. (1995) · Biometrics · doi:10.2307/2532976 · Cited by 2,017
Predicting chaotic time series
Farmer, J. D., & Sidorowich, J. J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.59.845 · Cited by 1,929
Non-linear time series. A dynamical system approach
Tong, H. (1990) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · Cited by 1,895
Non-linear Time Series: A Dynamical System Approach
Tong, H. (1990) · doi:10.1093/oso/9780198522249.001.0001 · Cited by 1,854
Cited by
Quantum computing for finance: Overview and prospects
Orús, R., Mugel, S., & Lizaso, E. (2019) · Reviews in Physics · doi:10.1016/j.revip.2019.100028 · Cited by 668
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Cheng, M., Fang, F., Kinouchi, T., Navon, I. M., & Pain, C. C. (2020) · Journal of Hydrology · doi:10.1016/j.jhydrol.2020.125376 · Cited by 312
A Machine Learning Approach to Predict Air Quality in California
Castelli, M., Clemente, F. M., Popovič, A., Silva, S., & Vanneschi, L. (2020) · Complexity · doi:10.1155/2020/8049504 · Cited by 298
Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. A. (2020) · Energies · doi:10.3390/en13020391 · Cited by 256
Data Poisoning Attacks against Autoregressive Models
Alfeld, S., Zhu, X., & Barford, P. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10237 · Cited by 205
Metadata
- Authors
- Bontempi, G., Ben Taieb, S., & Le Borgne, Y.-A.
- Venue
- Lecture Notes in Business Information Processing
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 62-77
- ISSN
- 1865-1348 (ISSN-L)
- 1865-1356
- OpenAlex
- W1547333707
- Cited by
- 531
- References
- 44
- FWCI
- 14.23
- Type
- conference paper
- 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: Machine Learning Strategies for Time Series Forecasting. https://app.cochat.ai/papers/10.1007/978-3-642-36318-4_3
- The paper
- Bontempi, G., Ben Taieb, S., & Le Borgne, Y.-A. (2013). Machine Learning Strategies for Time Series Forecasting. Lecture Notes in Business Information Processing. https://doi.org/10.1007/978-3-642-36318-4_3
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.
