A Hybrid CNN-LSTM model for temperature forecasting

Yahui Guo

doi:10.1117/12.2680802

23 May 2023 A Hybrid CNN-LSTM model for temperature forecasting

Yahui Guo

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264523 (2023) https://doi.org/10.1117/12.2680802
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

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.

Citation Download Citation

Yahui Guo "A Hybrid CNN-LSTM model for temperature forecasting", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264523 (23 May 2023); https://doi.org/10.1117/12.2680802

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