Embedded Files
Topics
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
Data & Analysis Files
Reading
Reading
Please read:
Please read:
Chapter 5 (without Section 5.5)
Assignment: Capturing trend and seasonality with regression
Assignment: Capturing trend and seasonality with regression
Chapter 5: Problem 1
Chapter 5: Problem 1
Chapter 9.1
Chapter 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)
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
Page updated
Google Sites
Report abuse