[2602.01776] Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting

Computer Science > Machine Learning arXiv:2602.01776 (cs) [Submitted on 2 Feb 2026 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting Authors:Mingyue Cheng, Xiaoyu Tao, Qi Liu, Ze Guo, Enhong Chen View a PDF of the paper titled Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting, by Mingyue Cheng and 4 other authors View PDF HTML (experimental) Abstract:Time series forecasting has traditionally been formulated as a model-centric, static, and single-pass prediction problem that maps historical observations to future values. While this paradigm has driven substantial progress, it proves insufficient in adaptive and multi-turn settings where forecasting requires informative feature extraction, reasoning-driven inference, iterative refinement, and continual adaptation over time. In this paper, we argue for agentic time series forecasting (ATSF), which reframes forecasting as an agentic process composed of perception, planning, action, reflection, and memory. Rather than focusing solely on predictive models, ATSF emphasizes organizing forecasting as an agentic workflow that can interact with tools, incorporate feedback from outcomes, and evolve through experience accumulation. We outline three representative implementation paradigms -- workflow-based design, agentic reinforcement learning, and a hybrid agentic workflow paradigm -- and discuss the opportunities and challen...