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I have time series data. Not sure whether the series is stationary or not. I applied adf.test and got a p-value much higher than 0.05 which I think means that the data is not stationary. I took differencing once to check the p-value again. And I see that p-value has gone below 0.05. Can I assume that the data is stationary and ready for forecast? Is there any other way to confirm this in 'R'?

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marked as duplicate by Stephan Kolassa, Peter Flom Jul 14 '18 at 12:17

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  • $\begingroup$ Did you made a plot before to jump into computations ? $\endgroup$ – AlainD Jul 10 '18 at 18:32
  • $\begingroup$ Yes.. plotted ACF and PACF as well, I could see autocorrelation in the data. Once I took differencing, I don't see autocorrelation and adf.test also shows p-value less than 0.05 $\endgroup$ – Vijaya Patil Jul 11 '18 at 17:11
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Yes and no. Yes, ADF test tells you that after differentiation your data became stationary. No, your data is not ready for forecasting yet.

Run your time-series model using stationary data first and then check for issues such as heteroskedasticity or autocorrelation before forecasting.

You can also examine residuals graph and ACF/PACF.

Forecasts are not accurate if heteroskedasticity/autocorrelation is present in the errors.

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  • $\begingroup$ The ADF test requires that there are no pulses, level shifts, seasonal pulses and local time trends in the series that need to be treated. $\endgroup$ – IrishStat Jul 11 '18 at 13:25
  • $\begingroup$ @Justina: Thanks for your help! I don't see autocorrelation in residuals. I will divide the data into train and test as suggested by you to be sure of forecasts. $\endgroup$ – Vijaya Patil Jul 11 '18 at 17:03

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