Reinforced Decoder: Toward Training Recurrent Neural Networks for Time-Series Forecasting

Recurrent neural network-based sequence-to-sequence (S2S) models have been extensively applied for multistep-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 inference 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. In addition, 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. Compared to the best baseline, the proposed method based on long short-term memory yields average relative improvements of 5.98% in root mean square error, 4.39% in mean absolute percentage error, and 7.63% in$\text{R}^{2}$. Furthermore, the proposed approach can also achieve promising performance when generalized to Transformer-based S2S forecasting models.