FiLM-SimVP: Scalable Uncertainty Quantification in Spatiotemporal Forecasting

<p dir="ltr">Uncertainty quantification in spatiotemporal forecasting is crucial for decision-making. Quantile regression has been proposed as a computationally efficient and assumption-free approach for uncertainty quantification by generating prediction intervals. Yet, existing dee...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Shehel Yoosuf (23073535) (author)
مؤلفون آخرون:	Hamza Baali (14603380) (author), Abdesselam Bouzerdoum (17900021) (author)
منشور في:	2025
الموضوعات:	Information and computing sciences Artificial intelligence Machine learning Quantile regression spatiotemporal forecasting probabilistic forecasting selfsupervised learning Computational modeling Predictive models Uncertainty Computer architecture Estimation Transformers Long short term memory Coordinate measuring machines
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FiLM-SimVP: Scalable Uncertainty Quantification in Spatiotemporal Forecasting

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