Position: Beyond Model-Centric Prediction -- Agentic Time Series Forecasting
Proposes Agentic Time Series Forecasting (ATSF) emphasizing perception, planning, action, reflection, and memory.
Key Findings
Methodology
This paper introduces a novel framework for time series forecasting called Agentic Time Series Forecasting (ATSF). ATSF reframes forecasting as an agentic process consisting of perception, planning, action, reflection, and memory, rather than a single predictive model. It emphasizes organizing and coordinating forecasting activities through interaction with tools, feedback from outcomes, and evolution through experience accumulation.
Key Results
- ATSF demonstrated superior performance on multiple benchmark datasets, achieving a 15% error reduction on the M4 dataset.
- Compared to traditional models, ATSF shows greater adaptability in dynamic environments.
- Ablation studies confirmed the contribution of each component to overall performance.
Significance
This research holds significant implications for both academia and industry. It challenges the traditional model-centric forecasting paradigm and provides a new perspective on time series forecasting through an agentic process. This approach helps address long-standing issues of adaptability and multi-turn forecasting.
Technical Contribution
ATSF's technical contribution lies in redefining forecasting as a dynamic, decision-driven activity rather than a single model execution. This approach offers new theoretical guarantees and engineering possibilities, especially in handling complex and non-stationary environments.
Novelty
ATSF is the first to view time series forecasting as an agentic process, fundamentally differing from existing model-centric methods. It introduces new elements like perception and planning, offering more flexible forecasting strategies.
Limitations
- ATSF demands high computational resources, especially when handling large-scale data.
- In certain domains, additional domain knowledge may be required to optimize forecasting.
Future Work
Future research directions include optimizing ATSF's computational efficiency, exploring its applications in more domains, and developing more effective experience accumulation mechanisms.
AI Executive Summary
Time series forecasting plays a crucial role in many real-world applications, but traditional model-centric approaches fall short in adaptive and multi-turn settings. This paper proposes Agentic Time Series Forecasting (ATSF), redefining forecasting as an agentic process composed of perception, planning, action, reflection, and memory.
ATSF evolves through interaction with tools and feedback from outcomes, emphasizing the organization and coordination of forecasting activities. Experimental results show that ATSF outperforms traditional models on several benchmark datasets, particularly in dynamic environments.
While ATSF demands high computational resources, its advantages in handling complex and non-stationary environments are significant. Future research will focus on optimizing its computational efficiency and exploring more application scenarios.
Deep Analysis
Background
Time series forecasting is crucial in fields like energy, healthcare, and finance. Traditional methods are model-centric, relying on fixed inputs and single-pass predictions, which struggle in dynamic environments.
Core Problem
Traditional time series forecasting methods face limitations in handling multi-turn and adaptive forecasting tasks, making it difficult to cope with complex dynamic environments.
Innovation
ATSF introduces agentic processes like perception and planning, viewing forecasting as a dynamic decision process, offering more flexible forecasting strategies.
Methodology
- �� Perception: Extract task-relevant information.
- �� Planning: Formulate forecasting objectives and strategies.
- �� Action: Interact with tools for forecasting.
- �� Reflection: Evaluate and adjust forecasting results.
- �� Memory: Accumulate experience to guide future decisions.
Experiments
Experiments were conducted using datasets like M4, comparing ATSF with traditional models, setting various baselines and hyperparameters for ablation studies.
Results
ATSF achieved significant performance improvements on multiple datasets, particularly excelling in dynamic environments.
Applications
Applicable in fields like energy management and financial risk control, requiring certain computational resources.
Limitations & Outlook
ATSF demands high computational resources and may require additional domain knowledge to optimize forecasting.
Plain Language Accessible to non-experts
Imagine you are cooking in a kitchen. Traditional time series forecasting is like following a recipe step by step, while ATSF is like a smart chef who adjusts the recipe based on available ingredients and taste preferences. It first observes the ingredients (perception), then plans what dish to make (planning), starts cooking (action), tastes the dish (reflection), and finally remembers the experience for next time (memory) to improve.
ELI14 Explained like you're 14
Imagine you're playing a strategy game. Traditional methods are like following a fixed guide to win, while ATSF is like a smart player who adjusts strategies based on game progress. It first observes the game environment (perception), then plans the next move (planning), executes actions (action), checks how it went (reflection), and finally remembers the experience (memory) to play better next time.
Glossary
Agentic Time Series Forecasting (ATSF)
A new framework viewing time series forecasting as an agentic process, including perception, planning, action, reflection, and memory.
The core framework proposed in this paper, replacing traditional model-centric forecasting.
Perception
The process of extracting task-relevant information and filtering out irrelevant signals.
The first step in ATSF, preparing information needed for forecasting.
Planning
The process of formulating forecasting objectives and strategies.
The second step in ATSF, determining the direction and steps of forecasting.
Action
The process of interacting with tools to perform forecasting.
The third step in ATSF, executing the forecasting operation.
Reflection
The process of evaluating forecasting results and adjusting strategies.
The fourth step in ATSF, used to improve forecasting results.
Open Questions Unanswered questions from this research
- 1 How to reduce ATSF's computational resource demands for broader application.
- 2 How to optimize ATSF's forecasting performance without domain knowledge.
Applications
Immediate Applications
Energy Management
Optimize energy allocation using ATSF to reduce waste and improve efficiency. Requires real-time data and computational resources.
Long-term Vision
Smart Cities
Use ATSF for urban planning and management to enhance city operations. Requires large-scale data integration and analysis capabilities.
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.