Analyzing and Predicting Temperature Data in Baoji: A SARIMA Model Approach

Abstract

In this study, I analyzed and predicted the temperature data of Baoji. First, I processed the data with outliers and explored the seasonal variation pattern of the temperature data through seasonal decomposition. Through the ADF unit root test, I confirm that the temperature data are non-stationary time series.

To make the prediction, I built the SARIMA model and adjusted the model parameters to get the best performance. I tried different autoregressive orders, difference orders, moving average orders, and seasonal periods, and selected the optimal model by comparing the model fitting effect and prediction ability.

Finally, I made a temperature prediction for some time in the future based on the SARIMA model I built. The forecast results show that the temperature shows a gradually increasing trend in the given time range. This conclusion is based on my evaluation of the model and analysis of the predicted results.

Although I have achieved some useful results through the analysis and prediction of temperature data, there are still some limitations to this study. The method of handling outliers in the data processing stage may need further improvement to improve the accuracy of the data. In addition, I only used the SARIMA model for the prediction, and the application of other time series models may yield more accurate results.

In summary, this study predicted the temperature data through time series analysis and SARIMA model and came to the conclusion that the temperature gradually increased. Future research can be carried out from the aspects of improving data processing methods and applying more time series models to improve the accuracy and reliability of temperature prediction.