Chronos: Learning the Language of Time Series

  • Chronos is a framework designed for pretrained probabilistic time series models.
  • It utilizes scaling and quantization to tokenize time series values into a fixed vocabulary.
  • Chronos trains transformer-based language model architectures (specifically, models from the T5 family with parameters ranging from 20M to 710M) using cross-entropy loss.
  • The models are pretrained on a mix of publicly available datasets and a synthetic dataset generated via Gaussian processes, enhancing generalization.
  • In a comprehensive benchmark involving 42 datasets, including both classical local models and deep learning approaches, Chronos models:
  • (a) significantly outperform other methods on datasets included in the training corpus;
  • (b) show comparable or occasionally superior zero-shot performance on new datasets compared to methods trained specifically on those datasets.
  • These results demonstrate the potential of pretrained models to leverage time series data across various domains for improving zero-shot accuracy on unseen forecasting tasks, suggesting a simplified approach to forecasting pipelines.