Exogenous Time Series Forecasting

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14 papers5 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting
Oct 19, 2026Yiheng Wang, Kai Zheng, Yuetan Lin +5Air Quality Prediction Spatiotemporal Forecasting
Fudan University · Shanghai Academy of AI for Science · University of Shanghai for Science and Technology
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Oct 14, 2026Chengyi Pan, Huangqimei Zheng, Hao XueTime Series Modeling Exogenous Time Series Forecasting
UGOD, HKUST-GZ · School of Software, Yunnan University · UGOD, The Hong Kong University of Science and Technology (Guangzhou)
RideBench: A Large-Scale Exogenous-Aware Benchmark for Ride-Hailing Time Series Forecasting
Oct 8, 2026Shengsheng Lin, Jing Hu, Zhengyang Hu +8Exogenous Time Series Forecasting Time Series Forecasting
South China University of Technology, China · Didi Chuxing, China · The University of Hong Kong, China
SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting
Oct 2, 2026Mingtian Tan, Palash Goyal, Mihir Parmar +6Time Series Forecasting Exogenous Time Series Forecasting
Google Cloud AI Research · University of Virginia
Foundations without Fundamentals: Zero-Shot Blind Spots in Time Series FMs
Oct 1, 2026Nafiseh Ghoroghchian, Haipeng Zhang, Shuyi Han +2Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
Layer6 AI
$t_0$ : A Time-Series Foundation Model for Forecasting with Context
Sep 21, 2026Lucas Meyer, Claudio Sole, Huikan Xiang +6Time Series Forecasting Zero-Shot Time Series Forecasting
The Forecasting Company · Macrocosm
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Sep 6, 2026Varsha Pendyala, Yiwei Fu, Weizhong Yan +1Exogenous Time Series Forecasting Electricity Load Forecasting
Artificial Intelligence Team GE Vernova Advanced Research Center, Niskayuna, NY, USA
Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting
Jul 8, 2026Qingzhong Li, Yue Hu, Hui Ma +4Exogenous Time Series Forecasting Time Series Forecasting
Xinjiang Key Laboratory of Intelligent Computing and Smart Applications, School of Software, Xinjiang University, Urumqi 830017, China. · College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China.
Exogenous Dropout: A Simple, Strong Baseline for Corruption-Robust Time Series Forecasting with Covariates
Jul 5, 2026Hao Hu, Xue-shan AiNeural Network Robustness Time Series Forecasting
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China · Hubei Key Laboratory of Water System Science for Sponge City Construction (Wuhan University), Wuhan 430072, China
Context-Aware Hospitalization Forecasting Evaluations for Decision Support using LLMs
Apr 27, 2026Rhea Makkuni, Ananya JoshiHealthcare Exogenous Time Series Forecasting
Johns Hopkins University Baltimore, USA
Time-Aware Prior Fitted Networks for Zero-Shot Forecasting with Exogenous Variables
Mar 16, 2026Andres Potapczynski, Ravi Kiran Selvam, Tatiana Konstantinova +7Exogenous Time Series Forecasting Time Series Forecasting
Amazon Science, New York University · Amazon Science
GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
Mar 9, 2026Zhengyu Li, Xiangfei Qiu, Yuhan Zhu +4Exogenous Time Series Forecasting Robust Time Series Forecasting
East China Normal University
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
Mar 5, 2026Jiafeng Lin, Mengren Zheng, Simeng Ye +5Exogenous Time Series Forecasting Time Series Forecasting
School of Software, BNRist, Tsinghua University, Beijing, China · Hongshen College, Chongqing University, Chongqing, China · School of Software, Tsinghua University, Beijing, China +1
DiTS: Multimodal Diffusion Transformers Are Time Series Forecasters
Feb 6, 2026Haoran Zhang, Haixuan Liu, Yong Liu +4Exogenous Time Series Forecasting Time Series Forecasting
School of Software, BNRist, Tsinghua University

Oct 19, 2026J/K move · Enter open · S save

AirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting

Yiheng Wang, Kai Zheng, Yuetan Lin, Fanglu Fan+4

Fudan University · Shanghai Academy of AI for Science · University of Shanghai for Science and Technology

Air Quality Prediction Spatiotemporal Forecasting Exogenous Time Series Forecasting Autoregressive Modeling

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Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems suffer from incomplete parameterizations, uncertain inputs, and high computational cost. Existing deep learning approaches improve efficiency, but often entangle meteorological and pollutant variables within unified forecasting frameworks, limiting flexibility and pollutant-specific representation learning. To address these challenges, we propose AirMPA, a decoupled autoregressive framework for meteorology-to-pollution forecasting. AirMPA predicts 13 atmospheric pollutants at 0.4° spatial resolution and 12-h temporal resolution using meteorological fields, emission priors, and historical pollutant concentrations. The model is built on an improved Swin-Transformer architecture with zonal release attention and dual-branch fusion, enabling more effective modeling of global transport continuity and heterogeneous pollutant dynamics. During training, AirMPA uses the fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) as meteorological forcing, while during inference it directly ingests forecast fields from external meteorological models, enabling stable autoregressive prediction up to 5 days ahead. Experiments show that AirMPA consistently outperforms Aurora and surpasses the Copernicus Atmosphere Monitoring Service (CAMS) in approximately 90% of pollutant-lead-time evaluation cases over the 5-day forecast horizon, while outperforming CAMS on all variables beyond 48 h. These results demonstrate the potential of decoupled meteorology-to-pollution forecasting for global air quality prediction.