title: Chronos: Learning the Language of Time Series
summary: 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.
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paper_authors.name: Huibin Shen
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paper_authors.name: Syama Sundar Rangapuram
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paper_authors.name: Michael W. Mahoney
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paper_authors.name: Kari Torkkola
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paper_authors.name: Andrew Gordon Wilson
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paper_authors.name: Yuyang Wang
paper_title: Chronos: Learning the Language of Time Series
paper_summary: 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.
paper_ai_summary: Chronos, a framework using pretrained transformer-based models for time series forecasting, outperforms classical methods and achieves comparable zero-shot performance on unseen datasets using tokenized time series data.
paper_ai_keywords: pretrained probabilistic time series models Chronos tokenization scaling quantization transformer-based language models cross-entropy loss T5 family Gaussian processes zero-shot performance forecasting tasks