Time series forecasting is central to decision-making in domains as diverse
as energy, finance, climate, and public health. In practice, forecasters face
thousands of short, noisy series that vary in frequency, quality, and horizon,
where the dominant cost lies not in model fitting, but in the labor-intensive
preprocessing, validation, and ensembling required to obtain reliable
predictions. Prevailing statistical and deep learning models are tailored to
specific datasets or domains and generalize poorly. A general, domain-agnostic
framework that minimizes human intervention is urgently in demand. In this
paper, we introduce TimeSeriesScientist (TSci), the first LLM-driven agentic
framework for general time series forecasting. The framework comprises four
specialized agents: Curator performs LLM-guided diagnostics augmented by
external tools that reason over data statistics to choose targeted
preprocessing; Planner narrows the hypothesis space of model choice by
leveraging multi-modal diagnostics and self-planning over the input; Forecaster
performs model fitting and validation and, based on the results, adaptively
selects the best model configuration as well as ensemble strategy to make final
predictions; and Reporter synthesizes the whole process into a comprehensive,
transparent report. With transparent natural-language rationales and
comprehensive reports, TSci transforms the forecasting workflow into a
white-box system that is both interpretable and extensible across tasks.
Empirical results on eight established benchmarks demonstrate that TSci
consistently outperforms both statistical and LLM-based baselines, reducing
forecast error by an average of 10.4% and 38.2%, respectively. Moreover, TSci
produces a clear and rigorous report that makes the forecasting workflow more
transparent and interpretable.