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ONLINE COURSE – Time Series Data Analysis (TSDA02) This course will be delivered live
15th November 2022 - 18th November 2022
£400.00
Event Date
Wednesday, November 16th, 2022
Course Format
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
TIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
About This Course
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.
Intended Audiences
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
Course Details
Availability – 30 places
Duration – 4 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
Teaching Format
The course will be divided into theoretical lectures to introduce and explain key concepts and theories. Afternoon practicals will be based on the topics covered in the morning lectures.
Assumed quantitative knowledge
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.
Assumed computer background
Attendees should already have experience with R and be able to read csv files, create simple plots, and manipulate data frames.
Equipment and software requirements
EQUIPMENT AND SOFTWARE REQUIREMENTS
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
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 oliverhooker@prstatistics.com. 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.
If you are unsure about course suitability, please get in touch by email to find out more oliverhooker@prstatistics.com
Course Programme
Wedesday 16th
| 9:30-10:30 | Introduction, example data sets |
| 10:30-10:45 | Coffee break |
| 10:45-11:45 | Revision: likelihood and inference |
| 11:45-12:00 | Break |
| 12:00-13:00 | Revision: linear regression and GLMs |
| 13:00-14:00 | Lunch |
| 14:00-14:45 | Tutor-guided practical: Loading data in R and running simple analysis |
| 14:45-15:00 | Coffee break |
| 15:00-17:00 | Self-guided practical: Using R for linear regression and GLMs’ |
Thursday 17th
| 9:30-10:30 | Auto-regressive models and random walks |
| 10:30-10:45 | Coffee break |
| 10:45-11:45 | Moving averages and ARMA |
| 11:45-12:00 | Break |
| 12:00-13:00 | Integrated models and ARIMA |
| 13:00-15:00 | Lunch |
| 15:00-15:45 | Tutor-guided practical: the forecast package in R |
| 15:45-16:00 | Coffee break |
| 16:00-17:00 | Self-guided practical: Fitting ARIMA models with forecast |
Friday 18th
| 9:30-10:30 | Including covariates: ARIMAX models |
| 10:30-10:45 | Coffee break |
| 10:45-11:45 | Creating bespoke time series models using Bayes |
| 11:45-12:00 | Break |
| 12:00-13:00 | Model choice and forecasting using Bayes |
| 13:00-14:00 | Lunch |
| 14:00-14:45 | Tutor-guided practical: a walkthrough example time series analysis |
| 14:45-15:00 | Coffee break |
| 15:00-17:00 | Self-guided practical: finding the best time series model for your data set |
Tuesday 22nd
| 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) |