A New Multipredictor Ensemble Decision Framework Based on Deep Reinforcement Learning for Regional GDP Prediction - Qingwen Li; Chengming Yu; Guangxi Yan (IEEE Access, 2022)
Gross domestic product (GDP) can effectively reflect the situation of economic development and resource allocation in different regions. The high-precision GDP prediction technology lays a foundation for the sustainable development of regional resources and the proposal of economic management policies. To build an accurate GDP prediction model, this paper proposed a new multi-predictor ensemble decision framework based on deep reinforcement learning. Overall modeling consists of the following steps: Firstly, GRU, TCN, and DBN are the main predictors to train three GDP forecasting models with their characteristics. Then, the DQN algorithm effectively analyses the adaptability of these three neural networks to different GDP datasets to obtain an ensemble model. Finally, by adaptive optimization of the ensemble weight coefficients of these three neural networks, the DQN algorithm got the final GDP prediction results. Through three groups of experimental cases from China, the following conclusions can be drawn: (1) the DQN algorithm can obtain excellent experimental results in ensemble learning, which effectively improves the prediction performance of single predictors by more than 10 %. (2) The ensemble multi-predictor region GDP prediction framework based on deep reinforcement learning can achieve better prediction results than 18 benchmark models. In addition, the MAPE value of the proposed model is lower than 4.2% in all cases.