Practice Time-series Cross-validation (12.5.B) - Model Evaluation and Validation

Practice: Time-Series Cross-Validation

Lesson 12.5.BModel Evaluation and Validation

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8 questions

Flashcard drill
3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the main purpose of time-series cross-validation?
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Think about why the order of data matters.
Question 2.
Name one technique used in time-series cross-validation.
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Remember we discussed a couple of methods.
3Question 3.
What is the main benefit of time-series cross-validation?
- It randomizes data splits
- It prevents future data from influencing training
- It allows for larger datasets in training
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Think about what is unique about time-series data.
4Question 4.
True or False: In rolling window validation, the training set size increases with each iteration.
- True
- False
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Reflect on how the training set evolves.
Question 5.
You have a dataset of monthly sales over five years and need to forecast future sales using both rolling and expanding techniques. Design an evaluation strategy for both.
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Think about the practical implications of each method.
Question 6.
Critique a model that relied solely on rolling methods in an unstable market context. What issues might arise?
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Consider the nature of the dataset.

4 more questions in this section. Enroll free to practise them all.