Integrating ARIMA with machine learning for temperature prediction in major cities in China

The changes in temperature may arise risks in many industries. To solve this problem, the National Meteorological Center and Dalian Commodity Exchange jointly compiled a temperature index which includes 5 cities. Therefore, forecasting time series temperature data in those cities is an important subject. Traditionally, we use statistic method ARIMA to predict the next lags of time series. With the advancement in computational power of computers and the introduction of more advanced machine learning algorithms, this paper develops a method by integrating ARIMA with machine learning to analyze and forecast time series data. The empirical studies conducted show that integrating ARIMA with Long Short-Term Memory outperforms that with Support Vector Regression, or Random Forest in their prediction accuracy.