Chronos: Learning the Language of Time Series
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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Abdul Fatir Ansari
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2024-03-13T13:39:07.378Z
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Abdul Fatir Ansari
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Lorenzo Stella
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2024-03-13T13:39:13.723Z
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Lorenzo Stella
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Caner Turkmen
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2024-03-13T13:39:26.000Z
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Caner Turkmen
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Xiyuan Zhang
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Xiyuan Zhang
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Pedro Mercado
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Pedro Mercado
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Huibin Shen
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2024-03-13T13:40:02.340Z
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Huibin Shen
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Oleksandr Shchur
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Oleksandr Shchur
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shchuro
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Syama Sundar Rangapuram
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Sebastian Pineda Arango
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Shubham Kapoor
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2024-03-13T13:40:33.311Z
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Shubham Kapoor
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Jasper Zschiegner
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Danielle C. Maddix
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2024-03-13T13:40:52.296Z
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Danielle Maddix
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Michael W. Mahoney
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Kari Torkkola
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Andrew Gordon Wilson
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Michael Bohlke-Schneider
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Yuyang Wang
Chronos: Learning the Language of Time Series
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.
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.
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