10.4 - Autocorrelation and Partial Autocorrelation
Enroll to start learning
You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take practice test.
Practice Questions
Test your understanding with targeted questions
What does ACF stand for?
💡 Hint: Look at the measurement related to time series correlation.
What does PACF measure?
💡 Hint: Think about measuring direct relationships in lags.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What does the ACF measure in time series?
💡 Hint: Think about how past values affect the current value.
True or False: PACF can help determine the order of MA models.
💡 Hint: Remember which function addresses lag control directly.
1 more question available
Challenge Problems
Push your limits with advanced challenges
You have a time series dataset with ACF values showing significant correlation up to lag 5, and a PACF showing significant correlation at lag 1 and none afterwards. How would you go about modeling this?
💡 Hint: Evaluate how ACF and PACF data reflect the dependencies when considering your model setup.
Recent data shows sharp declines in the ACF past lag 3, while the PACF shows substantial spikes up to lag 2. What type of model would be appropriate for this dataset?
💡 Hint: Pay attention to cut-off points in the ACF and PACF to guide your model choice.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.