A Practical Approach to Timeseries Forecasting Using Python - SARIMA Implementation
Interactive Video
•
Computers
•
11th - 12th Grade
•
Practice Problem
•
Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the first step in using SARIMA in Python?
Defining the seasonal order
Setting up the data frame
Importing the SARIMA model from statsmodels
Importing the pandas library
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the seasonal order in SARIMA help with?
Simplifying the data input process
Increasing the speed of computation
Reducing the complexity of the model
Improving the accuracy of predictions
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a key component of the SARIMA model setup?
Setting the random seed
Importing the matplotlib library
Specifying the seasonal order
Defining the data frame
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the 'results.summary()' function in SARIMA?
To save the model
To display the model's summary
To visualize the data
To import the necessary libraries
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main difference when performing predictions with SARIMA?
Using a different data frame
Changing the model parameters
Using 'results.predict' instead of 'model'
Altering the seasonal order
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the 'dynamic' parameter in the prediction function control?
The start date of predictions
The size of the plot
The end date of predictions
Whether predictions are updated with new data
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to import 'date offset' from pandas?
To improve model accuracy
To extend predictions to future dates
To simplify data visualization
To handle missing data
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