Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting

2026-02-02

AI Summary

Traditional time series forecasting is constrained by static, one-shot, model-centric paradigms that struggle to support dynamic reasoning and continual learning. This work proposes Agent-based Time Series Forecasting (ATSF), introducing an agent framework into the field for the first time and reconceptualizing prediction as a multi-round workflow encompassing perception, planning, action, reflection, and memory. ATSF enables tool invocation, feedback integration, and experiential evolution. Through three implementation pathways—workflow-based design, agent reinforcement learning, and hybrid agent architectures—ATSF establishes a novel forecasting paradigm that is interactive, evolvable, and supports iterative refinement. This study not only opens an agent-oriented research direction for time series forecasting but also systematically articulates its technical pathways, key challenges, and future opportunities.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Time-Series/Data Streams

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 challenges that arise when shifting from model-centric prediction to agentic forecasting. Together, this position aims to establish agentic forecasting as a foundation for future research at the intersection of time series forecasting.

Problem

• time series forecasting
• agentic forecasting
• model-centric prediction
• adaptive forecasting
• multi-turn reasoning

Innovation

• Agentic Time Series Forecasting
• Model-Centric Prediction
• Intelligent Agent Workflow
• Iterative Refinement
• Continual Adaptation