A Practical Approach to Timeseries Forecasting Using Python - Handling Non-Stationarity in Time Series

The video tutorial explains how to work with a dataset of international airline passengers. It covers loading the dataset, checking for stationarity using the Augmented Dickey-Fuller (ADF) test, and transforming non-stationary data into stationary data using methods like square root transformation. The tutorial concludes with verifying the stationarity of the transformed data and introduces a quiz to reinforce learning.

What are the two main components of the dataset introduced in the video?

• Time and passengers

• Date and weather

• Time and location

• Location and passengers

Which method is used to read the CSV file containing the dataset?

• NP.read_CSV

• PD.read_CSV

• NP.load_CSV

• PD.load_CSV

What indicates non-stationarity in the ADF test results?

• ADF statistics is less than critical values

• ADF statistics is negative

• ADF statistics is positive and greater than critical values

• ADF statistics is zero

Which of the following is NOT a method to make a series stationary?

• Log transformation

• Square root transformation

• Cube root transformation

• Exponential transformation

What transformation is applied to the dataset in the video?

• Log transformation

• Square root transformation

• Cube root transformation

• Exponential transformation

Why is it important to drop NA values after transformation?

• To make the data non-stationary

• To ensure data integrity and avoid errors

• To improve data visualization

• To increase the dataset size

What indicates that the series has become stationary after transformation?

• The value is greater than 0.05

• The value is positive

• The value is zero

• The value is less than 0.05 and negative