A Practical Approach to Timeseries Forecasting Using Python - Future Predictions Using SARIMA

The video tutorial explains how to use the pandas library to create future dates by applying date offsets. It demonstrates the process of building a DataFrame with these future dates and concatenating it with an existing DataFrame. The tutorial then covers forecasting using different models like ARIMA and auto ARIMA, emphasizing the importance of selecting the best model based on the data. The session concludes with a quiz to reinforce the learning.

What is the purpose of using date offsets in pandas?

• To delete rows from a data frame

• To create future dates based on a given offset

• To modify the structure of a data frame

• To sort data in a data frame

How do you specify the offset when creating future dates?

• By setting the offset in days

• By setting the offset in weeks

• By setting the offset in years

• By setting the offset in months

What is the first step in creating a future date data frame?

• Sorting the data

• Adding new columns

• Getting the dates

• Deleting old data

What should be ensured when creating a future date data frame?

• It should have more rows than the existing data frame

• It should have different columns from the existing data frame

• It should have fewer rows than the existing data frame

• It should have the same columns as the existing data frame

What is the purpose of concatenating the future data frame with the existing data frame?

• To prepare for predictions

• To delete unnecessary data

• To reduce the size of the data frame

• To change the data type

Which of the following is a method used for forecasting?

• K-Means Clustering

• ARIMA

• Linear Regression

• Decision Trees

What is a key consideration when choosing a forecasting model?

• The model should be the simplest

• The model should be the most complex

• The model should fit the data best

• The model should be the fastest to compute