Hey everyone,
i'm struggling with modelling some times series to get residuals with white noise characteristics. I use SPSS for ARIMA modelling and exponential smoothing and Gretl for stationary testing with the AugmentedDickeyFuller and the KPSSTest.
My workflow:
At first I use Gretl to test, if I need to differenciate or to transform my time series, to get stationary data. Gretl uses the ADF and the KPSStest to check, if the time series is stationary or not.
My second step is to check for autocorrelations and partial autocorrelations in the differenciated time series with SPSS and try to remove them by subtracting a suitable ARIMA model from the time series. I also always check, if I get residuals with lower autocorrelations and partial autocorrelations by using an exponential smoothing method with the time series modeler.
My last step is than to use Gretl and SPSS again to check if the residuals of my time series are stationary and have no significant autocorrelations and partial autocorrelations or in other words, to test if the time series are white noise processes, so I can cross correlate them afterwards.
My problem now is that I have used a ARIMA (1,1,1) model and a simple exponential smoothing model for modelling a specific time series, but in both cases I get residuals that aren’t stationary, indicated by a nonsignificant ADFTest in Gretl. I’ve tried several other models, but in all cased I got nonstationary residuals. Does somebody has an idea, how I can get rid of this problem?
Thanks in advance.
PS: In the attached files you see the plots of my raw time series and of the residuals I get after modelling my time series with a ARIMA(1,1,1) and a simple exponential smoothing model. I also attached the PACF, the ACF and the results of the ADFtests.
i'm struggling with modelling some times series to get residuals with white noise characteristics. I use SPSS for ARIMA modelling and exponential smoothing and Gretl for stationary testing with the AugmentedDickeyFuller and the KPSSTest.
My workflow:
At first I use Gretl to test, if I need to differenciate or to transform my time series, to get stationary data. Gretl uses the ADF and the KPSStest to check, if the time series is stationary or not.
My second step is to check for autocorrelations and partial autocorrelations in the differenciated time series with SPSS and try to remove them by subtracting a suitable ARIMA model from the time series. I also always check, if I get residuals with lower autocorrelations and partial autocorrelations by using an exponential smoothing method with the time series modeler.
My last step is than to use Gretl and SPSS again to check if the residuals of my time series are stationary and have no significant autocorrelations and partial autocorrelations or in other words, to test if the time series are white noise processes, so I can cross correlate them afterwards.
My problem now is that I have used a ARIMA (1,1,1) model and a simple exponential smoothing model for modelling a specific time series, but in both cases I get residuals that aren’t stationary, indicated by a nonsignificant ADFTest in Gretl. I’ve tried several other models, but in all cased I got nonstationary residuals. Does somebody has an idea, how I can get rid of this problem?
Thanks in advance.
PS: In the attached files you see the plots of my raw time series and of the residuals I get after modelling my time series with a ARIMA(1,1,1) and a simple exponential smoothing model. I also attached the PACF, the ACF and the results of the ADFtests.
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