Exogenous Time Series Forecasting

Latest papers

16 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence
Oct 28, 2026Madumita Karthikeyan, Umang Rajnikant Bid, Rong PanTime Series Forecasting Agentic Time Series Forecasting
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
A Granular Framework for Construction Material Price Forecasting: Econometric and Machine-Learning Approaches
Dec 10, 2025Boge Lyu, Qianye Yin, Iris Denise Tommelein +4Time Series Forecasting Exogenous Time Series Forecasting
Quotr, Floz Inc., Castro Valley, CA 94546, USA. · Baruch College, New York, NY 10010, USA. · University of California, Berkeley, Department of Civil and Environmental Engineering, 760 Davis Hall, Berkeley, CA 94720, USA. Berkeley, CA 94720, USA. +1

Oct 28, 2026J/K move · Enter open · S save

FACTS-MAS: A Multi-Agent Architecture for Adaptive Forecasting from Heterogeneous Evidence

Madumita Karthikeyan, Umang Rajnikant Bid, Rong Pan

Time Series Forecasting Agentic Time Series Forecasting Time Series Forecasting with Covariates Exogenous Time Series Forecasting

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Real-world forecasting often requires integrating heterogeneous evidence whose predictive value varies across horizons, operating regimes, and domains. We introduce FACTS-MAS, a domain-agnostic multi-agent forecasting framework that decomposes evidence into specialized channels for temporal dynamics, exogenous context, seasonality, structural information, and discrete events. Each agent generates an independently testable forecast through a common interface, while leakage-safe historical validation measures its predictive skill relative to a naive reference. These skill estimates are used to weight and combine agent forecasts, so that uninformative evidence is either removed or downweighted rather than forced to participate. A calibration procedure selects domain-specific constant means based on historical validation folds. We evaluate FACTS-MAS on weekly for-sale housing inventory from Zillow and weekly unemployment insurance claims from FRED, for 15 U.S. metropolitan areas at 4-, 8-, and 13-week forecasting horizons. Our method outperforms both a naive last-value forecast method and the TimesFM foundation model. However, a factor-adjusted exponential smoothing model performs as well as the fused forecast. The evidence therefore supports FACTS-MAS as a systematic protocol for testing, calibrating, and recomposing evidence channels before assembling them into the strongest single model for a specific application domain. Code, data, and results are available at https://github.com/Madumita24/FACTS-MAS.