Time Series Prediction Method for Meteorological Data Based on the ARIMA-LSTM Model

The purpose of this article is to explore and verify the effectiveness and advantages of ARIMA-LSTM hybrid model in meteorological data time series prediction. In this article, firstly, the meteorological data are pre-processed, including data cleaning, abnormal value detection and processing, and standardization operation to ensure the quality and consistency of the data. Based on this, the ARIMA-LSTM hybrid model is constructed. This model combines the ability of ARIMA (autoregressive integral moving average model) model in capturing linear relationship and short-term fluctuation, and the powerful ability of LSTM (long-term and short-term memory network) in dealing with nonlinear problems and long-term dependence. The experimental results show that the ARIMA-LSTM hybrid model has obvious advantages in forecasting meteorological data. Compared with single ARIMA model and LSTM network, the hybrid model has improved the prediction accuracy and stability. The model not only improves the prediction accuracy and stability, but also provides a reliable scientific basis for meteorological prediction and decision support.