FiLM-SimVP: Scalable Uncertainty Quantification in Spatiotemporal Forecasting

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...

Full description

Saved in:
Bibliographic Details
Main Author: Shehel Yoosuf (23073535) (author)
Other Authors: Hamza Baali (14603380) (author), Abdesselam Bouzerdoum (17900021) (author)
Published: 2025
Subjects:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items