Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
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Abstract (arXiv)
Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing the impact of shocks, such as the outbreak of the Russian-Ukrainian war. In this context, we apply deep neural network-based Granger causality to identify important drivers of natural gas demand. Furthermore, the resulting dependencies are used to construct a counterfactual case without the outbreak of the war, providing a quantifiable estimate of the overall effect of the shock on various German energy sectors. The code and dataset are available at https://github.com/bonaldli/CausalEnergy.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 15 Jul 2024.
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References · 17
- 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 97
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- 2022Jointly Contrastive Representation Learning on Road Network and Trajectorycited by 34
- 2019Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Predictioncited by 30
- 2023KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategycited by 19
- 2020Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profilecited by 16
- 2023MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysiscited by 13
- 2023Dynamic Causal Graph Convolutional Network for Traffic Predictioncited by 12
- 2024Online Test-Time Adaptation of Spatial-Temporal Traffic Flow Forecastingcited by 11
- 2024MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learningcited by 11
- 2023Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrappingcited by 9
- 2023Adaptive Hierarchical SpatioTemporal Network for Traffic Forecastingcited by 5
5 more
- 2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learningcited by 5
- 2024Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modelingcited by 5
- 2023Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clusteringcited by 4
- 2023Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clusteringcited by 4
- 2024MultiFun-DAG: Multivariate Functional Directed Acyclic Graphcited by 4
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