摘要：To address the complexity of urban economic data and the problem that traditional forecasting methods do not fully utilize the correlation of data, resulting in low prediction accuracy, an urban economic forecasting model based on the fusion of deep belief neural (DBN) and long-short term memory (LSTM) is proposed. The model is based on a combination of DBN and LSTM. The model first uses bandpass filtering to denoise the urban economic data and then determines the prediction starting point of the model based on the root-mean-square and cliff features in the trend diagram of the urban economy; secondly, the optimised 4-layer DBN network is used for deep feature extraction and training and testing of the LSTM. The reliability of the proposed model is demonstrated through urban economic experiments, and the prediction results are compared with those of traditional LSTM, BP (back propagation) neural network, and DBN-BP model to verify the effectiveness of the model.
