We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.

翻译：我们提出了Chronos，一个简洁而高效的预训练概率时间序列模型框架。Chronos通过缩放和量化将时间序列值标记化为固定词汇表，并基于交叉熵损失函数，在这些标记化的时间序列上训练现有的Transformer架构语言模型。我们基于T5系列（参数量从2000万到7.1亿不等）预训练了Chronos模型，使用大量公开数据集以及通过高斯过程生成的合成数据集（以提升泛化能力）进行训练。在包含42个数据集的全面基准测试中（涵盖经典局部模型与深度学习方法），我们证明Chronos模型：（a）在训练语料库包含的数据集上显著优于其他方法；（b）在面对全新数据集时，其零样本性能与针对这些数据集专门训练的方法相当，甚至偶尔更优。我们的结果表明，Chronos模型能够利用来自不同领域的时间序列数据，提升对未见预测任务的零样本准确性，从而使预训练模型成为大幅简化预测流程的可行工具。