Forecasting R Virtual Workshop
Event Information
About this Event
The NHS-R Community is running a Virtual Forecasting R Workshop from:
Monday 26th October - Friday 30th October, 10 - 12.30pm: It will compromise of five 2.5 hour sessions. Delegates must attend all sessions.
Content
There is a 1 hour pre-recorded video on: Data preparation and manipulation: how to prepare for the forecasting task, how to clean data? How to manipulate data to extract time series?
Session 1 - 2.5 hours
1.1.Forecasting and decision making: What is forecasting? How forecasting task is different from other modelling tasks? What is the link between forecasting and decision making, how to identify what to forecast?
1.2 Time series patterns and decomposition: what could be used in data for forecasting task? how to detect systematic pattern in the data? how to separate non-systematic pattern?
1.3. Forecaster’s toolbox: How to use time series graphics to identify patterns? what is a forecasting benchmark? What are the simple forecasting methods that could be used as benchmark? How to generate point forecasts and prediction interval using simple forecasting methods?
Session 2 - 2.5 hours
2.1 Forecast accuracy evaluation: How do we know if the forecasting method captures systematic patterns available in data? How to judge whether a forecast is accurate or not? How to evaluate the accuracy of point forecasts and prediction interval? Why do we need to distinguish between fitting and forecast?
Session 3 - 2.5 hours
3.1 Exponential smoothing models: What is the exponential smoothing family? what are available models in this family? What is captured by this family? how to generate point forecast and prediction intervals using exponential smoothing models?
Session 4 - 2.5 hours
4.1 ARIMA models: This is another important family of forecasting models. What is the ARIMA framework? what are available models in this family? What is captured by this family? how to generate point forecast and prediction intervals using ARIMA models?
Session 5 - 2.5 hours
5.1 Regression: We also look at causal techniques that consider external variables. What is the difference between regression and exponential smoothing and ARIMA? How to build a multiple regression model? How to use regressors and special events in a regression model?
