ONLINE COURSE – Time Series Data Analysis (TSDA01) This course will be delivered live
14 December 2021 - 17 December 2021£450
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email email@example.com for full details or to discuss how we can accommodate you).
This course covers introductory 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 5 days, covering the following topics:
- Revision; likelihood; time series data sets; linear and generalised linear models. Practical sessions on running glms in R and loading/saving data
- Autoregressive (AR) models; Autoregressive moving average (ARMA) models; integrated models (ARIMA). Practical sessions on using the
forecastpackage to fit ARIMA models
- 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
- Seasonality in time series data; stochastic volatility models; fitting Bayesian time series models. Practical session on fitting Bayesian time series models
- Continuous time series with Brownian motion and Ornstein-Uhlenbeck processes; change point analysis; multivariate time series analysis. Open afternoon session – bring your own data set
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.
Venue – Delivered remotely
Time zone – Western European Time
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact firstname.lastname@example.org. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of regression methods and generalised linear models.
Assumed computer background
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.
Equipment and software requirements
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.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK email@example.com
Tuesday 14th – Classes from 09:30 to 17:30
Wednesday 15th – Classes from 09:30 to 17:30
Thursday 16th – Classes from 09:30 to 17:30
Friday 17th – Classes from 09:30 to 17:30