Context-Aware Time Series Forecasting

Latest papers

8 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
Nov 3, 2026Yueqiao Chen, Dongyu LiuContext-Aware Time Series Forecasting Time Series Forecasting
UC Davis, Davis, CA, USA
GIFT-Eval-CTX: A Benchmark For Contextual Forecasting Model Evaluation
Oct 13, 2026Xu Liu, Hanshu Yan, Taha Aksu +6Context-Aware Time Series Forecasting Time Series Forecasting
Moirai Team, Salesforce AI Research
World2TS: Real-World Event Context for Time-Series Forecasting
Oct 10, 2026Zhiding Liu, Jiqian Yang, Mingyue Cheng +2Context-Aware Time Series Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, China · Shenzhen International Graduate School, Tsinghua University, China
Context-dependent time-series prediction via HyperReservoirs
Sep 28, 2026Kohei Tsuchiyama, Takatomo Mihana, Ryoichi Horisaki +1Context-Aware Time Series Forecasting Time Series Forecasting
Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
Beyond Numerical Time Series: A Unified Benchmark for Multimodal Forecasting with Heterogeneous Context
Sep 14, 2026Peng Chen, Zhihao Zhuang, Hongzhou Chen +6Context-Aware Time Series Forecasting Multivariate Time Series Forecasting
Ant International · Hangzhou, China
CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
Aug 4, 2026Xiaoyu Tao, Mingyue Cheng, Bokai Pan +6Context-Aware Time Series Forecasting Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, Anhui Province, China
Nexus : An Agentic Framework for Context-Aware Time Series Forecasting
May 14, 2026Sarkar Snigdha Sarathi Das, Palash Goyal, Mihir Parmar +6Context-Aware Time Series Forecasting Time Series Reasoning
Google · Pennsylvania State University
Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
Jan 23, 2026Haonan Yang, Jianchao Tang, Zhuo LiContext-Aware Time Series Forecasting Disentangled Representation Learning
National University of Defense Technology, Changsha, Hunan, China

Nov 3, 2026J/K move · Enter open · S save

CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting

Yueqiao Chen, Dongyu Liu

UC Davis, Davis, CA, USA

Context-Aware Time Series Forecasting Time Series Forecasting Time Series Forecasting with Covariates Time Series Transformers Mixture of Experts

Open paper AskPDF arXiv

We study context-aware time-series forecasting, in which a single target variable is predicted from its own history and a set of contextual variables, such as weather and calendar information. As the context becomes more diverse, a single model struggles to capture all of its regimes, while ensemble learning and Mixture-of-Experts approaches rely on manual data partitioning or soft routing, which increases the inference cost and hinders interpretability. We propose CATF, a Context-Aware Time-Series Forecasting framework built on a Manager-Worker architecture: a Transformer-based Manager routes each input to a single Worker based on the context, using hard routing during both training and inference. The Workers thus specialize without manual data partitioning, and any single-model forecaster can serve as a Worker. On five context-rich datasets, four horizons, and two normalization schemes, CATF improves over its standalone counterpart in 162 of 200 comparisons, the best CATF-enhanced model outperforms the best standalone baseline in 19 of 20 settings, and it outperforms the zero-shot Time-MoE foundation model in 15 of 20 settings. A case study on bike-sharing data shows that the Manager routes inputs by the calendar and that each Worker specializes in a different weekly pattern.