Copertina

Deconfounded Time Series Forecasting: A Causal Inference Approach

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Gao, Wentao, Du, Xiaojing, Yu, Wenjun, Chen, Xiongren, Guo, Yifan, Yang, Feiyu
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
di: Du, Xiaojing, et al.
Pubblicazione: (2024)

Deconfounding Multi-Cause Latent Confounders: A Factor-Model Approach to Climate Model Bias Correction
di: Gao, Wentao, et al.
Pubblicazione: (2024)

From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
di: Gao, Wentao, et al.
Pubblicazione: (2025)

TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
di: Gao, Wentao, et al.
Pubblicazione: (2024)

Revisiting Multivariate Time Series Forecasting with Missing Values
di: Yang, Jie, et al.
Pubblicazione: (2025)

Kolmogorov-Arnold Networks for Time Series Granger Causality Inference
di: Liu, Meiliang, et al.
Pubblicazione: (2025)

Causal Time-Series Synchronization for Multi-Dimensional Forecasting
di: Mayr, Michael, et al.
Pubblicazione: (2024)

Training and Evaluating Causal Forecasting Models for Time-Series
di: Crasson, Thomas, et al.
Pubblicazione: (2024)

Synergizing Deconfounding and Temporal Generalization For Time-series Counterfactual Outcome Estimation
di: Liu, Yiling, et al.
Pubblicazione: (2025)

Apollo-Forecast: Overcoming Aliasing and Inference Speed Challenges in Language Models for Time Series Forecasting
di: Yin, Tianyi, et al.
Pubblicazione: (2024)

Distilling Time Series Foundation Models for Efficient Forecasting
di: Li, Yuqi, et al.
Pubblicazione: (2026)

Test Time Learning for Time Series Forecasting
di: Christou, Panayiotis, et al.
Pubblicazione: (2024)

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
di: Hu, Yifan, et al.
Pubblicazione: (2025)

An Adversarial Learning Approach to Irregular Time-Series Forecasting
di: Nam, Heejeong, et al.
Pubblicazione: (2024)

Causal Graph Fuzzy LLMs: A First Introduction and Applications in Time Series Forecasting
di: Orang, Omid, et al.
Pubblicazione: (2025)

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
di: Zhou, Yitong, et al.
Pubblicazione: (2025)

A Mamba Foundation Model for Time Series Forecasting
di: Ma, Haoyu, et al.
Pubblicazione: (2024)

Estimating Peer Direct and Indirect Effects in Observational Network Data
di: Du, Xiaojing, et al.
Pubblicazione: (2024)

Semantically-Guided Inference for Conditional Diffusion Models: Enhancing Covariate Consistency in Time Series Forecasting
di: Ding, Rui, et al.
Pubblicazione: (2025)

CIER: A Novel Experience Replay Approach with Causal Inference in Deep Reinforcement Learning
di: Wang, Jingwen, et al.
Pubblicazione: (2024)

Wavelet Mixture of Experts for Time Series Forecasting
di: Zhou, Zheng, et al.
Pubblicazione: (2025)

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
di: Wu, Xingjian, et al.
Pubblicazione: (2025)

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces
di: Du, Jiayuan, et al.
Pubblicazione: (2026)

Auto-Regressive Moving Diffusion Models for Time Series Forecasting
di: Gao, Jiaxin, et al.
Pubblicazione: (2024)

FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities
di: Xiao, Jingge, et al.
Pubblicazione: (2024)

IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
di: Wu, Beiliang, et al.
Pubblicazione: (2025)

Inner-Instance Normalization for Time Series Forecasting
di: Jibao, Zipo, et al.
Pubblicazione: (2025)

Deconfounded Causality-aware Parameter-Efficient Fine-Tuning for Problem-Solving Improvement of LLMs
di: Wang, Ruoyu, et al.
Pubblicazione: (2024)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
di: Liang, Daojun, et al.
Pubblicazione: (2026)

The Rise of Diffusion Models in Time-Series Forecasting
di: Meijer, Caspar, et al.
Pubblicazione: (2024)

Deep Learning for Time Series Forecasting: A Survey
di: Kong, Xiangjie, et al.
Pubblicazione: (2025)

A Practical Approach to Causal Inference over Time
di: Cinquini, Martina, et al.
Pubblicazione: (2024)

Ister: Linear Transformer for Efficient Multivariate Time Series Forecasting
di: Cao, Fanpu, et al.
Pubblicazione: (2024)

KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting
di: Ding, Kuiye, et al.
Pubblicazione: (2025)

Diffusion Models for Time Series Forecasting: A Survey
di: Su, Chen, et al.
Pubblicazione: (2025)

Time Series Forecastability Measures
di: Wang, Rui, et al.
Pubblicazione: (2025)

Ellipsoidal Time Series Forecasting
di: Wang, Qilin
Pubblicazione: (2025)

Performative Time-Series Forecasting
di: Zhao, Zhiyuan, et al.
Pubblicazione: (2023)

Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive Learning
di: Gao, Haozhi, et al.
Pubblicazione: (2024)

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
di: Lu, Jiecheng, et al.
Pubblicazione: (2024)