Published in arXiv preprint, 2025
This paper presents a theoretical framework for nonlinear forecast reconciliation based on local curvature and projection geometry.
Recommended citation: Nespoli, L., Biswas, A., & Medici, V. (2025). "Geometry of Nonlinear Forecast Reconciliation." arXiv preprint arXiv:2507.22500.
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Published in Journal of Open Source Software, 2025
BayesReconPy implements probabilistic forecast reconciliation, including discrete and mixed hierarchies.
Recommended citation: Biswas, A., Nespoli, L., Azzimonti, D., Zambon, L., Rubattu, N., & Corani, G. (2025). "BayesReconPy: A Python package for forecast reconciliation." Journal of Open Source Software, 10(111), 8336. https://doi.org/10.21105/joss.08336
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Published in arXiv preprint, 2024
This paper proposes E‑STGCN, a novel model combining spatiotemporal GCNs and extreme value theory for accurate air quality forecasting.
Recommended citation: Panja, M., Chakraborty, T., Biswas, A., & Deb, S. (2024). "E‑STGCN: Extreme Spatiotemporal Graph Convolutional Networks for Air Quality Forecasting." arXiv preprint arXiv:2411.12258.
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