Chronos: Learning the Language of Time Series
Authors: name: Abdul Fatir Ansari, name: Lorenzo Stella, name: Caner Turkmen, name: Xiyuan Zhang, name: Pedro Mercado, name: Huibin Shen, name: Oleksandr Shchur, name: Syama Sundar Rangapuram, name: Sebastian Pineda Arango, name: Shubham Kapoor, name: Jasper Zschiegner, name: Danielle C. Maddix, name: Michael W. Mahoney, name: Kari Torkkola, name: Andrew Gordon Wilson, name: Michael Bohlke-Schneider, name: Yuyang Wang

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.