Stationarity Check

The video tutorial explains how to use the ADFuller test from the statsmodels library to check the stationarity of a time series. It covers importing the necessary tools, preparing the data, executing the test, and interpreting the results. The tutorial concludes by confirming the stationarity of the series and suggests moving on to deep learning models.

What is the primary purpose of using the Augmented Dickey-Fuller (ADF) test in time series analysis?

• To identify seasonal patterns

• To predict future values

• To calculate moving averages

• To check for stationarity in the data

Which Python library provides the adfuller function used for the ADF test?

• statsmodels

• numpy

• scikit-learn

• pandas

What does a p-value less than 0.05 indicate in the context of the ADF test?

• The test is inconclusive

• The series is stationary

• The series is non-stationary

• The series has a unit root

Which of the following is NOT a critical value level typically used in the ADF test?

• 10%

• 20%

• 5%

• 1%

What is the next step after confirming stationarity in a time series using the ADF test?

• Re-run the ADF test

• Perform a seasonal decomposition

• Move on to deep learning models

• Apply a square root transformation