Citing

If ns-sdn contributes to published work, cite the two papers and the software: the FLAIRS paper introduced the architecture, and the IEEE-ORSS paper is the formulation the package implements, with its spectral emission heads and the DGS10 experiments the defaults reproduce.

The papers

@article{sunder2026flairs,
  title   = {Non-Stationary Spectral Decomposition Network for Econometric Time Series Forecasting},
  author  = {Sunder, Nikhil},
  journal = {The International FLAIRS Conference Proceedings},
  volume  = {39},
  number  = {1},
  year    = {2026},
  doi     = {10.32473/flairs.39.1.141588}
}

@inproceedings{sunder2026orss,
  title     = {Non-Stationary Spectral Decomposition Network: Adaptive Spectral Emission Heads and Frequency Modulation},
  author    = {Sunder, Nikhil and Leon, Maikel},
  booktitle = {2026 IEEE Opportunity Research Scholars Symposium (ORSS)},
  address   = {Atlanta, GA, USA},
  publisher = {IEEE},
  pages     = {1--5},
  year      = {2026},
  doi       = {10.1109/ORSS71174.2026.11684282}
}

The software

@software{sunder_nssdn,
  author    = {Sunder, Nikhil},
  title     = {ns-sdn: the Non-Stationary Spectral Decomposition Network},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.23368625},
  url       = {nikhilxsunder/ns-sdn},
  license   = {MIT}
}

The DOI is the Zenodo concept DOI, which resolves to the latest release; every release is archived under its own DOI, listed on the Zenodo record. To pin a version in a citation, add version = {\ |release|\ } and cite that release’s DOI. The GitHub: CITATION.cff file in the repository carries the same entries in a form GitHub and Zenodo read directly.

Bibliography

Every work the documentation cites; citations elsewhere link here.

[1]

Nikhil Sunder and Maikel Leon. Non-stationary spectral decomposition network: adaptive spectral emission heads and frequency modulation. In 2026 IEEE Opportunity Research Scholars Symposium (ORSS), 1–5. Atlanta, GA, USA, 2026. IEEE. URL: https://ieeexplore.ieee.org/document/11684282, doi:10.1109/ORSS71174.2026.11684282.

[2]

Nikhil Sunder. Non-stationary spectral decomposition network for econometric time series forecasting. The International FLAIRS Conference Proceedings, 2026. URL: https://journals.flvc.org/FLAIRS/article/view/141588, doi:10.32473/flairs.39.1.141588.

[3]

James Durbin and Siem Jan Koopman. Time Series Analysis by State Space Methods. Oxford University Press, Oxford, 2 edition, 2012. doi:10.1093/acprof:oso/9780199641178.001.0001.

[4]

Norden E. Huang, Zheng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chyuan Yen, Chi Chao Tung, and Henry H. Liu. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971):903–995, 1998. doi:10.1098/rspa.1998.0193.

[5]

Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Advances in Neural Information Processing Systems, volume 33. 2020. URL: https://arxiv.org/abs/2006.09661, arXiv:2006.09661, doi:10.48550/arXiv.2006.09661.

[6]

Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using RNN encoder–decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734. Doha, Qatar, 2014. Association for Computational Linguistics. arXiv:1406.1078, doi:10.3115/v1/D14-1179.

[7]

Diederik P. Kingma and Jimmy Ba. Adam: a method for stochastic optimization. In International Conference on Learning Representations (ICLR). San Diego, CA, USA, 2015. arXiv:1412.6980, doi:10.48550/arXiv.1412.6980.

[8]

Cédric Villani. Optimal Transport: Old and New. Volume 338 of Grundlehren der mathematischen Wissenschaften. Springer, Berlin, Heidelberg, 2009. doi:10.1007/978-3-540-71050-9.