Module pre-requisites can be found
here. Please install these when you have a few spare minutes. All the raw files and code can be found here (click ‘Code > Download ZIP’ near the top right if you want offline versions of everything). All links below are in pdf format for you to annotate.
Please note that the course will be recorded so please turn off video if you do not wish to be shown on the recordings.
Day 1
| Time |
Class |
| 9:30-10:30 |
Introduction, example data sets (slides) |
| 10:30-10:45 |
Coffee break |
| 10:45-11:45 |
Revision: likelihood and inference (slides) |
| 11:45-12:00 |
Break |
| 12:00-13:00 |
Revision: linear regression and GLMs (slides) |
| 13:00-14:00 |
Lunch |
| 14:00-14:45 |
Tutor-guided practical: Loading data in R and running simple analysis (code) |
| 14:45-15:00 |
Coffee break |
| 15:00-17:00 |
Self-guided practical: Using R for linear regression and GLMs (worksheet) (answer code) |
Day 2
| Time |
Class |
| 9:30-10:30 |
Auto-regressive models and random walks (slides) |
| 10:30-10:45 |
Coffee break |
| 10:45-11:45 |
Moving averages and ARMA (slides) |
| 11:45-12:00 |
Break |
| 12:00-13:00 |
Integrated models and ARIMA (slides) |
| 13:00-14:00 |
Lunch |
| 14:00-14:45 |
Tutor-guided practical: the forecast package in R (code) |
| 14:45-15:00 |
Coffee break |
| 15:00-17:00 |
Self-guided practical: Fitting ARIMA models with forecast (worksheet) |
Day 3
| Time |
Class |
| 9:30-10:30 |
Including covariates: ARIMAX models (slides) |
| 10:30-10:45 |
Coffee break |
| 10:45-11:45 |
Creating bespoke time series models using Bayes (slides) |
| 11:45-12:00 |
Break |
| 12:00-13:00 |
Model choice and forecasting using Bayes (slides) |
| 13:00-14:00 |
Lunch |
| 14:00-14:45 |
Tutor-guided practical: a walkthrough example time series analysis (code) |
| 14:45-15:00 |
Coffee break |
| 15:00-17:00 |
Self-guided practical: finding the best time series model for your data set (worksheet) |
Day 4
| Time |
Class |
| 9:30-10:30 |
Modelling with seasonality and the frequency domain (slides) |
| 10:30-10:45 |
Coffee break |
| 10:45-11:45 |
Stochastic volatility models and heteroskedasticity (slides) |
| 11:45-12:00 |
Break |
| 12:00-13:00 |
Fitting Bayesian time series models (slides) |
| 13:00-14:00 |
Lunch |
| 14:00-14:45 |
Tutor-guided practical: fitting time series models in JAGS and Stan (code) |
| 14:45-15:00 |
Coffee break |
| 15:00-17:00 |
Self-guided practical: start analysing your own data set with Bayes (worksheet) |