Training course: Time Series Data Analysis

About

This course covers statistical modelling for the analysis of time series data. The main focus of the course is on data observed at regular (discrete) time points but later modules cover continuously-observed data. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular forecast package. The second half of the course looks at Bayesian time series analysis which is extremely customisable to bespoke data analysis situations.

The course is structured over 4 days, covering the following topics:

1. Revision; likelihood; time series data sets; linear and generalised linear models. Practical sessions on running glms in R and loading/saving data
2. Autoregressive (AR) models; Autoregressive moving average (ARMA) models; integrated models (ARIMA). Practical sessions on using the forecast package to fit ARIMA models
3. Including covariates in ARIMA models (ARIMAX); Bayesian inference for time series; time series analysis and model choice. Practical sessions on finding the best time series model for your data set
4. Seasonality in time series data; stochastic volatility models; fitting Bayesian time series models. Practical session on fitting Bayesian time series models

Intended audience

Research postgraduates, practicing academics, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data.

Pre-requisites

Participants should have :

• A basic understanding of regression methods and generalised linear models.
• Some familiarity with R including the ability to import/export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.
• A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and Mac and can be downloaded for free by following these links: R, Rstudio.

Start the course

Please install the software required for running the code here.

You can start browsing the course by visiting the timetable