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

Boyan Li; Pavel Loskot

doi:10.1117/12.2656015

12 January 2023 Integrating ARIMA with machine learning for temperature prediction in major cities in China

Boyan Li, Pavel Loskot

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 125092N (2023) https://doi.org/10.1117/12.2656015
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

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.

Citation Download Citation

Boyan Li and Pavel Loskot "Integrating ARIMA with machine learning for temperature prediction in major cities in China", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 125092N (12 January 2023); https://doi.org/10.1117/12.2656015

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