Open Access
| Issue |
E3S Web Conf.
Volume 714, 2026
2026 4th International Forum on Clean Energy Engineering (FCEE2026)
|
|
|---|---|---|
| Article Number | 04003 | |
| Number of page(s) | 17 | |
| Section | Data-Driven Prediction and Monitoring for Intelligent Energy Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202671404003 | |
| Published online | 08 June 2026 | |
- H.K. Alfares, M. Nazeeruddin, Int. J. Syst. Sci. 33, 23–34 (2002) [Google Scholar]
- R. Weron, Int. J. Forecasting 30, 1030–1081 (2014) [Google Scholar]
- G.E. Box, G.M. Jenkins, G.C. Reinsel, Time series analysis: forecasting and control, 5th ed. (John Wiley & Sons, Hoboken, NJ, 2015) [Google Scholar]
- S. Makridakis, E. Spiliotis, V. Assimakopoulos, PLOS ONE 15, e0194889 (2020) [Google Scholar]
- A. Vaswani et al., in Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, USA, vol. 30 (2017) [Google Scholar]
- S. Hochreiter, J. Schmidhuber, Neural Comput. 9, 1735–1780 (1997) [CrossRef] [PubMed] [Google Scholar]
- S. Bai, J.Z. Kolter, V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv:1803.01271 (2018) [Google Scholar]
- G.P. Zhang, Neurocomputing 50, 159–175 (2003) [CrossRef] [Google Scholar]
- J.D. Hamilton, Time series analysis (Princeton Univ. Press, Princeton, NJ, 1994) [Google Scholar]
- I. Goodfellow et al., in Advances in Neural Information Processing Systems (NIPS), Montreal, QC, Canada, vol. 27, 2672–2680 (2014) [Google Scholar]
- B. Lim, S.O. Arik, N. Loeff, T. Pfister, J. Mach. Learn. Res. 22, 1–41 (2021) [Google Scholar]
- Z. Sha et al., Graph-attention spatio-temporal networks for multivariate anomaly detection, arXiv:2211.07212 (2022) [Google Scholar]
- H. Zenati, M. Romain, C.-S. Foo, B. Lecouat, V. Chandrasekhar, Adversarially learned anomaly detection, arXiv:1801.04747 (2018) [Google Scholar]
- W.W. Wei, Time series analysis: univariate and multivariate methods, 2nd ed. (Pearson Education, Boston, MA, 2006) [Google Scholar]
- S.C. Fung, K.C. Lam, J. Appl. Stat. 24, 143–157 (1997) [Google Scholar]
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