Python Tutorial Augmented Dickey Fuller Test

In this micro video you will learn: Python Tutorial Augmented Dickey Fuller Test

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code snippet: ? AVAILABLE IN PINNED COMMENT BELOW


ADF Test:


This will perform the ADF test on the "Close" column of the dataframe and will output the test statistic and the p-value. The p-value is then compared with the significance level (e.g. 0.05) to determine whether to reject or fail to reject the null hypothesis of non-stationarity.

If the p-value is less than the significance level, it suggests that the time series is stationary and therefore it's mean, variance and autocorrelation structure are constant over time. On the other hand if the p-value is greater than the significance level it suggests that the time series is non-stationary, and therefore it has a trend or a unit root.

It is worth mentioning that the ADF test is not the only test available to check for stationary or non-stationary time series, and , other methods also exist such as KPSS test, PP test and others to confirm or reject the results of ADF test. Also different time series models are used to model stationary and non-stationary data, therefore interpreting the results of ADF test and time series modeling can help to select the appropriate model for the time-series data.



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