A Practical Approach to Timeseries Forecasting Using Python - Stationarity Check - Project 2: Microsoft Corporation Sto

The video tutorial explains how to use the ADFuller function from the statsmodels library to check the stationarity of a time series. It covers importing the function, preparing the data by converting a DataFrame column into a series, and executing the ADFuller test. The tutorial also details how to print the ADF statistic, P-value, and critical values, explaining the significance of these results. Finally, it concludes that if the P-value is less than 0.05, the series is stationary, and introduces the next topic of deep learning models.

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