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Computer Science > Machine Learning

arXiv:2403.07815 (cs)
[Submitted on 12 Mar 2024]

Title:Chronos: Learning the Language of Time Series

Authors:Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan
Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar
Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper
Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola,
Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
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Time Series, by Abdul Fatir Ansari and 16 other authors
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    Abstract: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.

Comments: Inference code and model checkpoints available at this
          https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:  arXiv:2403.07815 [cs.LG]
          (or arXiv:2403.07815v1 [cs.LG] for this version)

Submission history

From: Abdul Fatir Ansari [view email]
[v1] Tue, 12 Mar 2024 16:53:54 UTC (1,128 KB)
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