| Topic | Description | Link |
|---|---|---|
| Lesson | Starter Code | Link |
Dataset description: Airline Passengers data
After this lesson, students will be able to:
- Define forecasting.
- Define and identify trend and seasonality in time series data.
- Define and calculate autocorrelation manually.
- Generate and interpret a seasonal decomposition plot.
- Generate and interpret an autocorrelation plot.
- Generate and interpret a partial autocorrelation plot.
- Properly fit, generate predictions from, and evaluate a linear time series model.
Total Time: 120 mins
For supplemental reading material on this topic, check out the following resources:
- How to Handle Missing Data in Time Series Analysis
- Forecasting: Principles and Practice
- NIST Documentation on Time Series Analysis
- Peter Craigmile's Course on Time Series Analysis