Darts统一整合多个时序预测大模型，让零样本预测像调用普通模型一样简单。
Unified Zero-Shot Time Series Forecasting: A Darts Foundation
- 构建标准化接口，统一接入Chronos-2、TimesFM 2.5等多款大模型
- 支持零样本/微调预测、不确定性估计与回测，全流程无缝集成
- 只需改名即可替换原有模型，适合想快速试用大模型的研究者
自2020年发布以来，Darts已成为广泛使用的开源时间序列分析Python库。近期一系列基础模型在零样本预测中宣称取得准确率提升，预示着从训练定制模型转向使用预训练通用预测器的范式转变。然而，这些基础模型通常以孤立包形式发布，接口碎片化且与常用工具链兼容性差，导致联合评估和完整流程集成困难。在Darts中，我们开发了一组统一的\texttt{FoundationModel}类（Chronos-2、TimesFM 2.5、TiRex、PatchTST-FM），提供标准化、全周期的预测接口，外部依赖极低，便于将基础模型融入生态。现有Darts流水线只需更改模型名称即可使用基础模型；新流水线可支持零样本或微调预测、不确定性估计与回测，并结合数据处理与评估工具，在统一框架内完成端到端工作。
原文摘要 · Abstract (English)
Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis. A series of foundation models have recently claimed accuracy improvements in zero-shot forecasting, promising a paradigm shift from training custom models to harnessing pre-trained general-purpose forecasters. Foundation models, however, are often released as isolated packages with fragmented interfaces and limited interoperability with common tooling, making joint evaluation and integration within complete pipelines difficult. In Darts, we developed a unified $\texttt{FoundationModel}$ class collection (Chronos-2, TimesFM 2.5, TiRex, PatchTST-FM) that provides standardized, full-cycle forecasting interfaces with minimal external dependencies for integrating foundation models into the ecosystem. Existing Darts pipelines can now use foundation models with only a name change; new pipelines can use them for zero-shot or fine-tuned forecasting, uncertainty estimation, and backtesting, combined with data processing and evaluation tooling, all within a unified framework.
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