Seasonal Autoregressive Integrated Moving Average Model for Precipitation Time Series
By Xinghua Chang, Meng Gao, Yan Wang, X. Hou. 2012
65 citations recorded by the source corpus.
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.
Subfields: Environmental Engineering, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/50a9247547632e53366e4e6bf183b87ec282da24
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In Environmental Engineering
In Water Science and Technology
Citations in the corpus
- Modelling and Forecasting of Rainfall Time Series Using SARIMA (2017, methodology): “These include AR, MA, and ARIMA, SARIMA models of different orders (Yule 1927; Walker 1931; Salas et al. 1980: Modareres 2007; Nirmala and Sundaram 2010; Asklany et al. 2011; Chang et al. 2012; Yusof and Kane 2012; Meshram et al. 2015; Hassan and Mohamed 2014; Akpanta et al. 2015).”