AN EARLY WARNING SYSTEM FOR FINANCIAL CRISES: A TEMPORAL CONVOLUTIONAL NETWORK APPROACH
提出基于时间卷积网络的金融危机早期预警系统，与逻辑回归等模型对比，发现其预测效果更优，且股价和实际GDP增长贡献最大。
The widespread and substantial effect of the global financial crisis in history underlines the importance of forecasting financial crisis effectively. In this paper, we propose temporal convolutional network (TCN), which based on a convolutional neural network, to construct an early warning system for financial crises. The proposed TCN is compared with logit model and other deep learning models. The Shapley value decomposition is calculated for the interpretability of the early warning system. Experimental results show that the proposed TCN outperforms other models, and the stock price and the real GDP growth have the largest contributions in the crises prediction.