A Study on China's Monthly Temperature Based on the Seasonal Autoregressive Integrated Moving Average

Research Article
Open access

A Study on China's Monthly Temperature Based on the Seasonal Autoregressive Integrated Moving Average

Chenjie Hu 1*
  • 1 Department of Mathematics, University of Toronto, Toronto, Canada    
  • *corresponding author chenjie.hu@mail.utoronto.ca
AEMPS Vol.170
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-019-1
ISBN (Online): 978-1-80590-020-7

Abstract

This study employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to analyze and forecast China’s monthly average temperature data from January 1990 to December 2010, using data from 2011 to 2013 as the test set. The objective is to explore temperature trends and provide a scientific basis for short-term temperature forecasting. Compared with the standard ARIMA model, SARIMA is more effective in modeling data with seasonality. Since the original dataset meets the stationarity requirements, it was directly used for model fitting. The model parameters—including autoregressive, moving average, seasonal autoregressive, and seasonal moving average terms—were automatically selected using R code to ensure accurate fitting of the temperature time series. The results show that the SARIMA model effectively captures both seasonal fluctuations and long-term trends in temperature, yielding reliable short-term forecasts. The final model, , achieves a Mean Absolute Percentage Error (MAPE) of 8.37% on the test set, meeting the expected level of predictive accuracy. The model's forecasting capability offers valuable support for climate policymaking, adaptive strategies to climate change, and sustainable development planning.

Keywords:

SARIMA model, temperature forecasting, time series analysis

Hu,C. (2025). A Study on China's Monthly Temperature Based on the Seasonal Autoregressive Integrated Moving Average. Advances in Economics, Management and Political Sciences,170,60-66.
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References

[1]. Yang, Y., Fan, C. and Xiong, H. (2022) A Novel General-Purpose Hybrid Model for Time Series Forecasting: A Novel General-Purpose Hybrid Model for Time Series Forecasting. Applied Intelligence (Dordrecht, Netherlands), 52, 2212-2223.

[2]. Chen, P., Niu, A., Liu, D., Jiang, W. and Ma, B. (2018) Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing. IOP Conference Series. Materials Science and Engineering, 394, 052024.

[3]. Denton, G. H., Alley, R. B., Comer, G. C. and Broecker, W. S. (2005) The Role of Seasonality in Abrupt Climate Change. Quaternary Science Reviews, 24, 1159-1182.

[4]. Bueh, C., Shi, N. and Xie, Z. (2011) Large‐scale Circulation Anomalies Associated with Persistent Low Temperature Over Southern China in January 2008. Atmospheric Science Letters, 12, 273-280.

[5]. Gillett, N. P., Kirchmeier-Young, M., Ribes, A., Shiogama, H., Hegerl, G. C., Knutti, R., Gastineau, G., John, J. G., L, L., Nazarenko, L., Rosenbloom, N., Seland, Ø., Wu, T., Yukimoto, S. and Ziehn, T. (2021) Constraining Human Contributions to Observed Warming since the Pre-Industrial Period. Nature Climate Change, 11, 207-212.

[6]. Sim, S., Kim, D. and Bae, H. (2023) Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 14266-14283.

[7]. Shao, X. (2011) Testing for White Noise Under Unknown Dependence and its Applications to Diagnostic Checking for Time Series Models. Econometric Theory, 27, 312-343.

[8]. Ray, S., Das, S. S., Mishra, P. and Al Khatib, A. M. G. (2021) Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries. Earth Systems and Environment, 5, 531-546.

[9]. Jobst, D., Möller, A., and Groß, J. (2024) Time‐series‐based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing. Quarterly Journal of the Royal Meteorological Society, 150, 4838-4855.

[10]. Zhang, K., Huo, X., and Shao, K. (2023) Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network. Mathematics (Basel), 11, 2060.


Cite this article

Hu,C. (2025). A Study on China's Monthly Temperature Based on the Seasonal Autoregressive Integrated Moving Average. Advances in Economics, Management and Political Sciences,170,60-66.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN:978-1-80590-019-1(Print) / 978-1-80590-020-7(Online)
Editor:Florian Marcel Nuţă
Conference website: https://2025.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.170
ISSN:2754-1169(Print) / 2754-1177(Online)

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References

[1]. Yang, Y., Fan, C. and Xiong, H. (2022) A Novel General-Purpose Hybrid Model for Time Series Forecasting: A Novel General-Purpose Hybrid Model for Time Series Forecasting. Applied Intelligence (Dordrecht, Netherlands), 52, 2212-2223.

[2]. Chen, P., Niu, A., Liu, D., Jiang, W. and Ma, B. (2018) Time Series Forecasting of Temperatures using SARIMA: An Example from Nanjing. IOP Conference Series. Materials Science and Engineering, 394, 052024.

[3]. Denton, G. H., Alley, R. B., Comer, G. C. and Broecker, W. S. (2005) The Role of Seasonality in Abrupt Climate Change. Quaternary Science Reviews, 24, 1159-1182.

[4]. Bueh, C., Shi, N. and Xie, Z. (2011) Large‐scale Circulation Anomalies Associated with Persistent Low Temperature Over Southern China in January 2008. Atmospheric Science Letters, 12, 273-280.

[5]. Gillett, N. P., Kirchmeier-Young, M., Ribes, A., Shiogama, H., Hegerl, G. C., Knutti, R., Gastineau, G., John, J. G., L, L., Nazarenko, L., Rosenbloom, N., Seland, Ø., Wu, T., Yukimoto, S. and Ziehn, T. (2021) Constraining Human Contributions to Observed Warming since the Pre-Industrial Period. Nature Climate Change, 11, 207-212.

[6]. Sim, S., Kim, D. and Bae, H. (2023) Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 14266-14283.

[7]. Shao, X. (2011) Testing for White Noise Under Unknown Dependence and its Applications to Diagnostic Checking for Time Series Models. Econometric Theory, 27, 312-343.

[8]. Ray, S., Das, S. S., Mishra, P. and Al Khatib, A. M. G. (2021) Time Series SARIMA Modelling and Forecasting of Monthly Rainfall and Temperature in the South Asian Countries. Earth Systems and Environment, 5, 531-546.

[9]. Jobst, D., Möller, A., and Groß, J. (2024) Time‐series‐based Ensemble Model Output Statistics for Temperature Forecasts Postprocessing. Quarterly Journal of the Royal Meteorological Society, 150, 4838-4855.

[10]. Zhang, K., Huo, X., and Shao, K. (2023) Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network. Mathematics (Basel), 11, 2060.