Research on Temperature Prediction Based on LSTM

Abstract

Temperature change has an important influence on human health and the development of all walks of life, so accurate temperature forecast is becoming more and more important. With the dramatic increase in the scale and dimension of meteorological data, it poses a new challenge to temperature prediction. In the era of big data, traditional time series forecasting methods have been difficult to deal with massive meteorological data. Temperature data is a kind of time series data with obvious non-stationary fluctuation characteristics. Modeling the temperature data can analyze the temperature change. Aiming at the problem that the prediction accuracy of time series model is not high, this paper proposes to build a temperature prediction model based on long-term and short-term memory network LSTM deep learning network, and divide the training set prediction set with Jena climate data set to evaluate the accuracy of the model. The experimental results show that the long-term memory network LSTM deep learning network performs well in the temperature prediction model.