Abstract
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.
Highlights
Autoregressive Integrated Moving Average Model (ARIMA)(p, d, q) model, we should consider some periodical time series
The SARIMA (p, 0, q)(P, 1, Q)12 model could be fitted to the de-seasonalized data
From Autocorrelation Function (ACF) of the stationary series, we can see the ACF peak at h =1s; while for Partial Autocorrelation Function (PACF), it peaks at h =1s, 2s,...,6s
Summary
Autoregressive Integrated Moving Average Model (ARIMA), is a widely used time series analysis model in statistics. We can build pure seasona A and Jenkins in the early 1970s, which is often termed as ARIMA(P,D,Q) model (He, 2004) with the time series. ARIMA is a kind of short-term parameters P, D and Q are the relevant seasonal prediction model in time series analysis. Because this autoregressive parameter, seasonal integrated parameter method is relatively systematic, flexible and can grasp more original time series information, it is widely used in meteorology, engineering technology, Marine, economic statistics and prediction technology, (Kantz and Schreiber, 2004; Cryer and Chan, 2008)
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