Financial Time Series Forecasting with the Deep Learning Ensemble Model

With the continuous development of financial markets worldwide to tackle rapid changes such as climate change and global warming, there has been increasing recognition importance time series forecasting in market operation management. In this paper, we propose a new model based on deep learning ensemble model. The is constructed by taking advantage convolutional neural network (CNN), long short-term memory (LSTM) network, autoregressive moving average (ARMA) CNN-LSTM introduced spatiotemporal data feature, while ARMA used autocorrelation feature. These models are combined framework mixture linear nonlinear features series. empirical results using show that proposed ensemble-based achieved superior performance terms accuracy robustness compared with benchmark individual models.