A Hybrid CNN-LSTM model for temperature forecasting

Abstract

Temperature prediction has always been a difficult point in meteorological prediction. It not only affects the global climate and ecosystem, but also has a profound impact on people 's social practice. In recent years, the temperature prediction mostly adopts the method of constructing neural network, because the method of decision tree, statistics and vector regression ignores the time series dependence, but the neural network-based models have limited its accuracy. Many scholars like to use CNN or LSTM model alone, and both of them are still lacking. Therefore, this paper proposes to combine CNN and LSTM models, which can not only give full play to the advantages of CNN model in processing data quickly, but also use the memory function of LSTM model to process temperature data with strong time series. The experimental results show that the CNN-LSTM model is more accurate than the CNN and LSTM models alone in predicting temperature.