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Abstract


Detection of Whether The Autocorrelated Meteorological Time Series Have Stationarity by Using Unit Root Approach: The Case of Tokat
This paper presents methodologies on detecting nonstationarity and removing nonstationarity by differencing for the autocorrelated time series. For this purpose, 5-year daily temperature, light intensity and relative humidity data from weather station in Tokat were used as materials. Since 5-year daily data sequences include many records, the new data sequences were constituted by averaging daily average temperature and relative humidity (average of records at 7.00, 14.00 and 21.00 in a day) and daily light intensity of five years. The existence of serial correlation between the averages from the mentioned climatic components was examined by using graphical approach, Ljung-Box Q and Runs tests. These three approaches imply that the averages have serial correlation. Unit root test (augmented Dickey and Fuller, ADF) was applied to test whether the averaged daily data sequences are nonstationary. The results of ADF emphasis the existence of nonstationarity in the daily data sequences. Similarly, the autocorrelation function graphs (correlogram) show nonstationarty, because of slowly decay in the autocorrelation functions for the daily data sequences. The first differencing was applied to remove nonstationary. After taking the first differencing, the ADF test results and the correlograms of the daily data sequences showed stationary.

Keywords
Unit root, Dickey and Fuller test, Ljung-Box Q statistic, runs test.


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