  In recent years, generative pre-trained paradigms such as Large Language
Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary
advancements and widespread real-world applications. Particularly, the
emergence of pre-trained LLMs-based temporal works, compared to previous deep
model approaches, has demonstrated superior generalization and robustness,
showcasing the potential of generative pre-trained paradigms as foundation
models for time series. However, those LLMs-based works mainly focus on
cross-modal research, i.e., leveraging the language capabilities of LLMs in
time series contexts. Although they have achieved impressive performance, there
still exist the issues of concept drift caused by differences in data
distribution and inflexibility caused by misalignment of dimensions. To this
end, inspired by recent work on LVMs, we reconsider the paradigm of time series
modeling. In this paper, we comprehensively explore, for the first time, the
effectiveness and superiority of the Generative Pre-trained Diffusion (GPD)
paradigm in real-world multivariate time series forecasting (TSF).
Specifically, to mitigate performance bias introduced by sophisticated
networks, we propose a straightforward MLP diffusion network for unconditional
modeling of time series. Then we employ a zero-shot and tuning-free method to
predict (generate) future data using historical data as prompts. The GPD
paradigm is established on the time series modality, effectively preventing the
phenomenon of concept drift, and enabling flexible forecasting of arbitrary
lengths. We demonstrate that the GPD paradigm achieves comprehensive
performance and generalization comparable to current SOTA LLM-based and deep
model paradigms on mainstream benchmarks and various TSF tasks. Extensive
experiments validate the potential of the GPD paradigm and its assistance in
future related research.
