The sequence prediction theory of wavelet neural network is applied to short-term temperature prediction. By using wavelet function as the activation function of the hidden layer of BP neural network, a "compact" wavelet neural network prediction model is established. The structural characteristics of the model are analyzed and the specific steps of building the model are described. Based on two sets of temperature observation data, internal characteristics and constraints of different data series are revealed using statistical analysis. Then, short-term temperature changes are predicted using wavelet neural network and predicted results are compared with the actual temperature. Finally, the prediction accuracy of wavelet neural network based on different data sequences is compared and analyzed. The results show that the wavelet neural network has a good accuracy for the prediction of short-term temperature change.

wavelet neural network, temperature, prediction, time series, correlation coefficient