Research Article Open Access

Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series

Yan Wang1, Xinghua Chang1, Meng Gao2 and Xiyong Hou2
  • 1 Yantai University, China
  • 2 , China


Predicting the trend of precipitation is a difficult task in meteorology and environmental sciences. Statistical approaches from time series analysis provide an alternative way for precipitation prediction. The ARIMA model incorporating seasonal characteristics, which is referred to as seasonal ARIMA model was presented. The time series data is the monthly precipitation data in Yantai, China and the period is from 1961 to 2011. The model was denoted as SARIMA (1, 0, 1) (0, 1, 1)12 in this study. We first analyzed the stability and correlation of the time series. Then we predicted the monthly precipitation for the coming three yesrs. The results showed that the model fitted the data well and the stochastic seasonal fluctuation was sucessfuly modeled. Seasonal ARIMA model was a proper method for modeling and predicting the time series of monthly percipitation.

Journal of Mathematics and Statistics
Volume 8 No. 4, 2012, 500-505


Submitted On: 7 September 2012 Published On: 20 February 2013

How to Cite: Wang, Y., Chang, X., Gao, M. & Hou, X. (2012). Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series. Journal of Mathematics and Statistics, 8(4), 500-505.

  • 44 Citations



  • Time Series Analysis
  • SARIMA Model
  • Forecasting