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Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Openstl: A comprehensive benchmark of spatio-temporal predictive learning
Spatio-temporal predictive learning is a learning paradigm that enables models to learn
spatial and temporal patterns by predicting future frames from given past frames in an …
spatial and temporal patterns by predicting future frames from given past frames in an …
Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting
Deep learning approaches have been widely adopted for precipitation nowcasting in recent
years. Previous studies mainly focus on proposing new model architectures to improve pixel …
years. Previous studies mainly focus on proposing new model architectures to improve pixel …
Advection Augmented Convolutional Neural Networks
Many problems in physical sciences are characterized by the prediction of space-time
sequences. Such problems range from weather prediction to the analysis of disease …
sequences. Such problems range from weather prediction to the analysis of disease …
Tian**ng: A linear complexity transformer model with explicit attention decay for global weather forecasting
S Yuan, G Wang, B Mu, F Zhou - Advances in Atmospheric Sciences, 2025 - Springer
In this paper, we introduce Tian**ng, a transformer-based data-driven model designed with
physical augmentation for skillful and efficient global weather forecasting. Previous data …
physical augmentation for skillful and efficient global weather forecasting. Previous data …
A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction
Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of
the ocean and climate systems. Establishing spatio-temporal correlation among input data is …
the ocean and climate systems. Establishing spatio-temporal correlation among input data is …
Deterministic guidance diffusion model for probabilistic weather forecasting
Weather forecasting requires not only accuracy but also the ability to perform probabilistic
prediction. However, deterministic weather forecasting methods do not support probabilistic …
prediction. However, deterministic weather forecasting methods do not support probabilistic …
CausalVE: Face Video Privacy Encryption via Causal Video Prediction
Advanced facial recognition technologies and recommender systems with inadequate
privacy technologies and policies for facial interactions increase concerns about bioprivacy …
privacy technologies and policies for facial interactions increase concerns about bioprivacy …
Deep-Learning-Based Daytime COT Retrieval and Prediction Method Using FY4A AGRI Data
The traditional method for retrieving cloud optical thickness (COT) is carried out through a
Look-Up Table (LUT). Researchers must make a series of idealized assumptions and …
Look-Up Table (LUT). Researchers must make a series of idealized assumptions and …
A 3D-CNN and multi-loss video prediction architecture
Z Qin, Q Dai - Applied Intelligence, 2025 - Springer
The achievements of deep learning in the sphere of computer vision have elevated video
prediction to a prominent research focus. The prevailing trend in current deep learning …
prediction to a prominent research focus. The prevailing trend in current deep learning …