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Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model
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Citations
Cited by
Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model
Zhang, L., Lin, J., Qiu, R., Hu, X., Zhang, H., Chen, Q., Tan, H., Lin, D., & Wang, J. (2018) · Ecological Indicators · doi:10.1016/j.ecolind.2018.08.032 · Cited by 304
Al-Musaylh, M. S., Deo, R. C., Adamowski, J. F., & Li, Y. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2017.11.002 · Cited by 300
State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach
Yun, Z., Qin, W., Shi, W., & Ping, P. (2020) · Energies · doi:10.3390/en13184858 · Cited by 33
Metadata
- Authors
- Yuan, C., Liu, S., & Fang, Z.
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 100
- Pages
- 384-390
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2275543810
- Cited by
- 393
- References
- 0
- FWCI
- 44.16
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. https://app.cochat.ai/papers/10.1016/j.energy.2016.02.001
- The paper
- Yuan, C., Liu, S., & Fang, Z. (2016). Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model. Energy. https://doi.org/10.1016/j.energy.2016.02.001
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.