Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
<|reference_start|>Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction: We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.<|reference_end|>
@article{feng2024only,
    title={Only the Curve Shape Matters: Training Foundation Models for Zero-Shot
  Multivariate Time Series Forecasting through Next Curve Shape Prediction},
    author={Cheng Feng, Long Huang, Denis Krompass},
    journal={arXiv preprint arXiv:2402.07570},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.07570},
    primaryClass={cs.LG cs.AI}
}