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.

本研究提出了一种名为增强解码器的新型训练方法，通过引入辅助模型生成备用解码器输入来解决多步骤时间序列预测中的累积误差问题，并利用强化学习算法动态选择最优输入以提高准确性。综合实验表明，该方法在多个数据集上优于常见训练方法。此外，该方法在基于自注意力的序列到序列预测模型中同样表现良好。