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.

我们提出了通用时间转换器（GTT），这是一个仅基于编码器的风格基础模型，用于零样本多变量时间序列预测。实验结果表明，GTT 在未见过的时间序列数据集上展现出优越的预测能力，甚至超过了最先进的监督基线模型。此外，我们还研究了 GTT 模型参数和训练数据集规模变化的影响，观察到缩放定律在零样本多变量时间序列预测中也成立。