  In this paper, we introduce ProNet, an novel deep learning approach designed
for multi-horizon time series forecasting, adaptively blending autoregressive
(AR) and non-autoregressive (NAR) strategies. Our method involves dividing the
forecasting horizon into segments, predicting the most crucial steps in each
segment non-autoregressively, and the remaining steps autoregressively. The
segmentation process relies on latent variables, which effectively capture the
significance of individual time steps through variational inference. In
comparison to AR models, ProNet showcases remarkable advantages, requiring
fewer AR iterations, resulting in faster prediction speed, and mitigating error
accumulation. On the other hand, when compared to NAR models, ProNet takes into
account the interdependency of predictions in the output space, leading to
improved forecasting accuracy. Our comprehensive evaluation, encompassing four
large datasets, and an ablation study, demonstrate the effectiveness of ProNet,
highlighting its superior performance in terms of accuracy and prediction
speed, outperforming state-of-the-art AR and NAR forecasting models.
