Practice Time-Series Cross-Validation - 12.5.B | 12. Model Evaluation and Validation | Data Science Advance
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Time-Series Cross-Validation

12.5.B - Time-Series Cross-Validation

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the main purpose of time-series cross-validation?

💡 Hint: Think about why the order of data matters.

Question 2 Easy

Name one technique used in time-series cross-validation.

💡 Hint: Remember we discussed a couple of methods.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

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

💡 Hint: Think about what is unique about time-series data.

Question 2

True or False: In rolling window validation, the training set size increases with each iteration.

True
False

💡 Hint: Reflect on how the training set evolves.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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.

💡 Hint: Think about the practical implications of each method.

Challenge 2 Hard

Critique a model that relied solely on rolling methods in an unstable market context. What issues might arise?

💡 Hint: Consider the nature of the dataset.

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Reference links

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