  Unlike natural language processing and computer vision, the development of
Foundation Models (FMs) for time series forecasting is blocked due to data
scarcity. While recent efforts are focused on building such FMs by unlocking
the potential of language models (LMs) for time series analysis, dedicated
parameters for various downstream forecasting tasks need training, which
hinders the common knowledge sharing across domains. Moreover, data owners may
hesitate to share the access to local data due to privacy concerns and
copyright protection, which makes it impossible to simply construct a FM on
cross-domain training instances. To address these issues, we propose Time-FFM,
a Federated Foundation Model for Time series forecasting by leveraging
pretrained LMs. Specifically, we begin by transforming time series into the
modality of text tokens. To bootstrap LMs for time series reasoning, we propose
a prompt adaption module to determine domain-customized prompts dynamically
instead of artificially. Given the data heterogeneity across domains, we design
a personalized federated training strategy by learning global encoders and
local prediction heads. Our comprehensive experiments indicate that Time-FFM
outperforms state-of-the-arts and promises effective few-shot and zero-shot
forecaster.
