Temperature time series prediction based on autoregressive integrated moving average model

Temperature time series prediction based on autoregressive integrated moving average model

Huanhuan ZhengYuxiu Bai Yaqiong Zhang 

School of Information Engineering, Yulin University, Yulin 719000, China

Corresponding Author Email: 
497759682@qq.com
Page: 
443-453
|
DOI: 
https://doi.org/10.3166/I2M.17.443-453
Received: 
|
Accepted: 
|
Published: 
30 September 2018
| Citation

OPEN ACCESS

Abstract: 

This paper establishes a prediction model for land and ocean temperature time series based on the improved autoregressive integrated moving average (ARIMA) model. First, the temperature time series was normalized and differenced before passing the stationarity test by augmented Dickey-Fuller (ADF) method, while the model parameters were determined by the autocorrelation coefficient and the partial autocorrelation coefficient. After that, the model was trained by the historical temperature data series, and applied to predict the temperatures in future. To validate the model, several experiments were conducted using the average land and ocean temperature data of Lawrence Berkeley National Laboratory. The results of the ARIMA-based model were contrasted against those of the support vector regression (SVR) and the random forest (RF). The comparison shows that the ARIMA-based model was 10%~30% smaller than the SVR and the RF in the values of RMSE and MAE, and 1%~10% higher in the value of R2. This means our model outperformed the two benchmark algorithms.

Keywords: 

autoregressive integrated moving average (ARIMA) model, temperature prediction, time series analysis, difference, stationarity test

1. Introduction
2. ARIMA-based prediction model
3. Experimental verification
4. Conclusions
Acknowledgement

This paper is made possible thanks to the generous support from High-Level Talent Research Startup Fund of Yulin University (Grant No.: 14GK46); Yulin Science and Technology Plan Project (Grant No.: 2016CXY-12-6); Special Scientific Research Plan of Shaanxi Provincial Department of Education (Grant No.: 18JK0902)

  References

Das M., Ghosh S. K. (2017). Sembnet: A semantic Bayesian network for multivariate prediction of meteorological time series data. Pattern Recognition Letters, pp. 93. https://doi.org/10.1016/j.patrec.2017.01.002

Erdemir D., Ayata T. (2016). Prediction of temperature decreasing on a green roof by using artificial neural network. Applied Thermal Engineering, pp. 112. https://doi.org/10.1016/j.applthermaleng.2016.10.145

Grigorievskiy A., Miche Y., Ventelä A. M., Séverin E., Lendasse A. (2013). Long-term time series prediction using OP-ELM. Neural Networks, Vol. 51, pp. 50-56. https://doi.org/10.1016/j.neunet.2013.12.002

Korteby Y., Mahdi Y., Azizou A., Daoud K., Regdon G. (2016). Implementation of an artificial neural network as a PAT tool for the prediction of temperature distribution within a pharmaceutical fluidized bed granulator. European Journal of Pharmaceutical Sciences, pp. 88. https://doi.org/10.1016/j.ejps.2016.03.010

Mesbah M., Soroush E. (2016). Development of a least square support vector machine model for prediction of natural gas hydrate formation temperature. Chinese Journal of Chemical Engineering, Vol. 25, No. 9, pp. 1238-1248. https://doi.org/10.1016/j.cjche.2016.09.007

Mohammadi K., Shamshirband S., Motamedi S., Petković D., Hashim R., Gocic M. (2015). Extreme learning machine based prediction of daily dew point temperature. Computers and Electronics in Agriculture, Vol. 117, pp. 214-225. https://doi.org/10.1016/j.compag.2015.08.008

Rheinwalt A., Boers N., Marwan N., Kurths J., Hoffmann P., Gerstengarbe F. W., Werner P. (2016). Non-linear time series analysis of precipitation events using regional climate networks for Germany. Climate Dynamics, Vol. 46, No. 3-4, pp. 065-1074. https://doi.org/10.1007/s00382-015-2632-z

Shirvani A., Nazemosadat S. M. J., Kahya E. (2015). Analyses of the Persian gulf sea surface temperature: Prediction and detection of climate change signals. Arabian Journal of Geosciences, Vol. 8, No. 4, pp. 2121-2130. https://doi.org/ 10.1007/s12517-014-1278-1

Tu Y. D., Yi Y. P. (2017). Forecasting cointegrated nonstationary time series with time-varying variance. Journal of Econometrics, Vol. 196, No. 1. https://doi.org/10.1016/j.jeconom.2016.09.012

Wang L., Wang Z. G., Liu S. (2016). An effective multivariate time series classification approach using echo state network and adaptive differential evolution algorithm. Expert Systems with Applications, Vol. 43, pp. 237-249. https://doi.org/10.1016/j.eswa.2015.08.055

Xu B., Dan H. C., Li L. (2017). Temperature prediction model of asphalt pavement in cold regions based on an improved BP neural network. Applied Thermal Engineering, pp. 120. https://doi.org/10.1016/j.applthermaleng.2017.04.024

Xue Y., Forman B. A. (2015). Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and the Advanced Microwave Scanning Radiometer. Remote Sensing of Environment, Vol. 170, pp. 153-165. https://doi.org/10.1016/j.rse.2015.09.009