Open Access
Issue
ITM Web Conf.
Volume 88, 2026
The 2026 International Conference on Artificial Intelligence, Big Data and Computer Science (AIBDCS 2026)
Article Number 01033
Number of page(s) 6
Section Artificial Intelligence, Big Data and Computer Science
DOI https://doi.org/10.1051/itmconf/20268801033
Published online 27 July 2026
  1. Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194). [Google Scholar]
  2. Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in Neural Information Processing Systems, 34, 22419–22430. [Google Scholar]
  3. Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021, May). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 12, pp. 11106–11115). [CrossRef] [Google Scholar]
  4. Wood, K., Kessler, S., Roberts, S. J., & Zohren, S. (2023). Few-shot learning patterns in financial time-series for trend-following strategies. arXiv preprint arXiv:2310.10500. [Google Scholar]
  5. Blasco, T., Sánchez, J. S., & García, V. (2024). A survey on uncertainty quantification in deep learning for financial time series prediction. Neurocomputing, 576, 127339. [Google Scholar]
  6. Wang, Y., Yao, Q., Kwok, J. T., & Ni, L. M. (2020). Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys, 53(3), 1–34. [Google Scholar]
  7. Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (pp. 1126–1135). PMLR. [Google Scholar]
  8. Gregnanin, M., Smedt, J. D., Gnecco, G., & Parton, M. (2023, September). Stock price time series forecasting using dynamic graph neural networks and attention mechanism in recurrent neural networks. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 357–373). Cham: Springer Nature Switzerland. [Google Scholar]
  9. Vaiciukynas, E., Danenas, P., Kontrimas, V., & Butleris, R. (2021). Two-step meta-learning for time-series forecasting ensemble. IEEE Access, 9, 62687–62696. [Google Scholar]
  10. Zhang, H., Guo, H., Bai, H., Zhang, C., & Li, L. (2025). MAML-based temporal supervised information maximizing GAN for few-shot time series data generation. Expert Systems with Applications, 129342. [Google Scholar]
  11. Wei, B., Hei, Y., & Wan, Y. (2025). Attention-based spatial-temporal interactive couple neural networks for multivariate time series forecasting. Information Sciences, 122647. [Google Scholar]
  12. Satrya, W. F., & Yun, J. H. (2023). Combining model-agnostic meta-learning and transfer learning for regression. Sensors, 23(2), 583. [Google Scholar]

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