## Topics- Linear regression refresher
- Regression for forecasting vs. inference
- Linear, exponential, polynomial trends
- Additive and multiplicative seasonality
- De-trending and seasonal adjustment
- Using the model for generating forecasts
- Measuring predictive accuracy
- Global vs. local patterns
- Irregular patterns
## Data & Analysis Files## Slides | ## ReadingPlease read: Chapter 5 (without Section 5.5) ## Assignment: Capturing trend and seasonality with regressionChapter 5: Problem 1Chapter 9.1 - How can a regression model be fitted to the demand series, given the inter-day and intra-day cycles? Mention the response (Y) and predictor variables (X’s) in your suggested regression model.
Chapter 5: Problem 4 (requires AR material)In this assignment, you will investigate the effect of an event on a time series by generating pre-event forecasts using linear regression. You will use linear regression to capture patterns such as trend and seasonality, and gain experience using XLMiner for generating regression-based forecasts. Download September 11 Travels from ForecastingBook.com/datasets |