  Multi-horizon time series forecasting, crucial across diverse domains,
demands high accuracy and speed. While AutoRegressive (AR) models excel in
short-term predictions, they suffer speed and error issues as the horizon
extends. Non-AutoRegressive (NAR) models suit long-term predictions but
struggle with interdependence, yielding unrealistic results. We introduce
AMLNet, an innovative NAR model that achieves realistic forecasts through an
online Knowledge Distillation (KD) approach. AMLNet harnesses the strengths of
both AR and NAR models by training a deep AR decoder and a deep NAR decoder in
a collaborative manner, serving as ensemble teachers that impart knowledge to a
shallower NAR decoder. This knowledge transfer is facilitated through two key
mechanisms: 1) outcome-driven KD, which dynamically weights the contribution of
KD losses from the teacher models, enabling the shallow NAR decoder to
incorporate the ensemble's diversity; and 2) hint-driven KD, which employs
adversarial training to extract valuable insights from the model's hidden
states for distillation. Extensive experimentation showcases AMLNet's
superiority over conventional AR and NAR models, thereby presenting a promising
avenue for multi-horizon time series forecasting that enhances accuracy and
expedites computation.
