  The success of large pretrained models in natural language processing (NLP)
and computer vision (CV) has opened new avenues for constructing foundation
models for time series forecasting (TSF). Traditional TSF foundation models
rely heavily on numerical data fitting. In contrast, the human brain is
inherently skilled at processing visual information, prefer predicting future
trends by observing visualized sequences. From a biomimetic perspective,
utilizing models to directly process numerical sequences might not be the most
effective route to achieving Artificial General Intelligence (AGI). This paper
proposes ViTime, a novel Visual Intelligence-based foundation model for TSF.
ViTime overcomes the limitations of numerical time series data fitting by
utilizing visual data processing paradigms and employs a innovative data
synthesis method during training, called Real Time Series (RealTS). Experiments
on a diverse set of previously unseen forecasting datasets demonstrate that
ViTime achieves state-of-the-art zero-shot performance, even surpassing the
best individually trained supervised models in some situations. These findings
suggest that visual intelligence can significantly enhance time series analysis
and forecasting, paving the way for more advanced and versatile models in the
field. The code for our framework is accessible at
https://github.com/IkeYang/ViTime.
