Practice Common Challenges in Time Series - 10.11 | 10. Time Series Analysis and Forecasting | Data Science Advance
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define missing data in time series analysis.

πŸ’‘ Hint: Think about data points that are not recorded.

Question 2

Easy

What is an outlier?

πŸ’‘ Hint: Consider extreme values that stand apart.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is a common issue caused by missing data?

  • Inaccurate predictions
  • Increased data volume
  • Enhanced reliability

πŸ’‘ Hint: Consider how absence affects prediction ability.

Question 2

True or False: Outliers should always be removed from a dataset to improve model accuracy.

  • True
  • False

πŸ’‘ Hint: Think about the nature of outliers.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are working with a sales forecasting model, and you notice that every December, sales spike unusually due to holiday trends. How would you handle these spikes when defining outliers?

πŸ’‘ Hint: Consider seasonality patterns and regular trends.

Question 2

A company observes that customer behavior is changing significantly over the years, reflected in their purchase patterns. How could they identify and address concept drift in their forecasting models?

πŸ’‘ Hint: Think about continuous validation and adaptation.

Challenge and get performance evaluation