Unified Training of Universal Time Series Forecasting Transformers
<|reference_start|>Unified Training of Universal Time Series Forecasting Transformers: Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capable of addressing diverse downstream forecasting tasks. However, constructing such a model poses unique challenges specific to time series data: i) cross-frequency learning, ii) accommodating an arbitrary number of variates for multivariate time series, and iii) addressing the varying distributional properties inherent in large-scale data. To address these challenges, we present novel enhancements to the conventional time series Transformer architecture, resulting in our proposed Masked Encoder-based Universal Time Series Forecasting Transformer (Moirai). Trained on our newly introduced Large-scale Open Time Series Archive (LOTSA) featuring over 27B observations across nine domains, Moirai achieves competitive or superior performance as a zero-shot forecaster when compared to full-shot models. Code, data, and model weights can be found at https://github.com/SalesforceAIResearch/uni2ts.<|reference_end|>
@article{woo2024unified,
    title={Unified Training of Universal Time Series Forecasting Transformers},
    author={Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio
  Savarese, Doyen Sahoo},
    journal={arXiv preprint arXiv:2402.02592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02592},
    primaryClass={cs.LG cs.AI}
}