  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.
