Keywords
winters
ARIMA models
autocorrelation
periodogramm.
How to Cite
Abstract
In the series of monthly number of earthquakes is present long-term systematic component. The assumption about stationary of the average value and the variance is rejected. Statistically significant autocorrelation coefficients mean that the time series is not random, and there is some connection between successive levels. In order to predict a series of exponential smoothing method was used. Best model is simple seasonal for the logarithm of the levels, and Winters additive for the square root level of the original series. The research of time series confirms that the ARIMA (0, 1, 2) x (1, 0, 1) s model can be used to the analysis and prediction of uniform non-stationary time series with a nonlinear trend, such as a polynomial of low degree. Prediction for 2012 is computed using simple Simple seasonal, Winters and ARIMA models.
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