  Recurrent neural network-based sequence-to-sequence models have been
extensively applied for multi-step-ahead time series forecasting. These models
typically involve a decoder trained using either its previous forecasts or the
actual observed values as the decoder inputs. However, relying on
self-generated predictions can lead to the rapid accumulation of errors over
multiple steps, while using the actual observations introduces exposure bias as
these values are unavailable during the extrapolation stage. In this regard,
this study proposes a novel training approach called reinforced decoder, which
introduces auxiliary models to generate alternative decoder inputs that remain
accessible when extrapolating. Additionally, a reinforcement learning algorithm
is utilized to dynamically select the optimal inputs to improve accuracy.
Comprehensive experiments demonstrate that our approach outperforms
representative training methods over several datasets. Furthermore, the
proposed approach also exhibits promising performance when generalized to
self-attention-based sequence-to-sequence forecasting models.
