Module pre-requisites can be found here. All the raw files and code can be found here. Click ‘Download ZIP’ near the top right if you want offline versions of everything. A pre-course questionnaire is avialable here.

Tuesday 10th May

Time Class
9:15-10:00 Time series analysis in climatology and ecology: some examples and general goals (DM) Module 1
10:00-10:45 Discussion and coffee
10:45-11:30 An introduction to Bayesian statistics (AP) Module 2
11:30-12:00 Discussion and break
12:00-12:45 The JAGS software with simple examples (AP) Module 3
12:45-14:00 Lunch
14:00-14:45 The JAGS software with simple examples (continued) Module 3
14:45-15:00 Break
15:00-16:30 Practical: Revision of R and introduction to JAGS (DM) Practical 1

Wednesday 11th May

Time Class
9:15-10:00 AR(1) models and Random walks (DM) Module 4
10:00-10:45 Discussion and coffee
10:45-11:30 MA and ARIMA models (AP) Module 5
11:30-12:00 Discussion and break
12:00-12:45 ARIMAX, model choice, and forecasting (AP) Module 6
12:45-14:00 Lunch
14:00-15:30 Practical: Fitting ARIMA models in JAGS (DM) Practical 2
15:30-16:00 Break
16:00-17:30 Discussion: Advantages and disadvantages of ARIMA modelling

Thursday 12th May

Time Class
9:15-10:00 Models with changing variance and seasonality (AP) Module 7
10:00-10:45 Discussion and coffee
10:45-11:30 Models for continuous time series (AP) Module 9
11:30-12:00 Discussion and break
12:00-12:45 Gaussian processes for time series (DM) Module 8
12:45-14:00 Lunch
14:00-15:30 Fitting Gaussian processes in JAGS (DM) Practical 3
15:30-16:00 Break
16:00-17:30 Discussion: Gaussian Processes

Friday 13th May

Time Class
9:15-10:00 Extensions: state space models and multivariate time series (AP) Module 10
10:00-10:45 Discussion and coffee
10:45-11:30 Practical 4: Bring your own data (AP and DM)
11:30-12:00 Break
12:00-12:45 Bring your own data (continued)
12:45-14:00 Lunch
14:00-17:30 Bring your own data (continued)