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Found 4 papers, 1 papers with code
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.
Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data
Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method.
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
