ONLINE COURSE – Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live
17 September 2024 - 26 September 2024
£480.00
Event Date
Tuesday, October 17th, 2024
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.
Course Program
TIME ZONE – Central Time Zone – 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.
Course Details
In this six-day course, we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas, such as finance, meteorology, ecology, public policy, and health. We start by introducing the concepts of time series and stationarity, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then, we will introduce benchmark forecasting methods, namely the naïve (or random walk) method, mean, drift, and seasonal naïve methods. After that, we will present different exponential smoothing methods (simple, Holt’s linear method, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally, we will cover Bayesian implementations of time series models and introduce extended models, such as ARCH, GARCH and stochastic volatility models, as well as Brownian motion and Ornstein-Uhlenbeck processes.
Intended Audiences
This course is aimed at anyone who is interested in forecasting methods,
and using R for data science or statistics. R is widely used in all areas of
academic scientific research, and also widely throughout the public, and
private sector.
Venue
Delivered remotely
Course Information
Time zone – Central Time Zone
Availability – TBC
Duration – 3 days
Contact hours – Approx. 14 hours
ECT’s – Equal to 1 ECT’s
Language – English
Teaching Format
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R.
Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at: 6pm-9pm
All sessions will be video recorded and made available to all attendees as soon as possible. If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Assumed quantitative knowledge
A basic understanding of R and statistical concepts. Specifically, linear regression models, statistical significance, and hypothesis testing.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
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.
Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).
A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.
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.
COURSE PROGRAMME
Tuesday 17th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 1
Section 1: Introductory concepts in time series analysis. White noise, stationarity, autocovariance and autocorrelation.
Section 2: Useful plots in time series analysis. Time plots, seasonal plots, autocorrelation plots. Time series decomposition: additive and multiplicative using the fable package in R.
Wednesday 18th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 2
Section 3: Time series decomposition: additive and multiplicative using the fable package in R.
Section 4: Benchmark forecasting methods. The naïve, mean, drift, and seasonal naïve methods. Cross-validation methods for time series analysis.
Time series plots (Independant practical 1) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.
Thursday 19th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 3
Section 4 (‘ctd)
Section 5: Exponential smoothing. Simple exponential smoothing, Holt’s linear method, Holt-Winters seasonal
method, and fable’s general ETS method.
Time series decomposition and benchmark forecasting methods (Independant practical 2) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.
Tuesday 24th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 4
Section 6: Autoregressive (AR) and moving-average (MA) models. Unit root tests for stationarity. How to identity the order of an AR(p) or an MA(q) model using autocorrelation and partial autocorrelation plots.
Section 7: Autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. Automatic order selection for a (seasonal) ARIMA model using fable. Linear regression with ARIMA errors.
Exponential smoothing (Independant practical 3) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.
Wednesday 25th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 5
Section 8: Generalized Additive Models (GAMs). An introduction to semi-parametric regression using splines. Incorporating trends and seasonal components of a time series using a GAM.
Section 9: An introduction to Bayesian modelling. Implementation of random walks, autoregressive, and moving average models using JAGS.
ARIMA models (Independant practical 4) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.
Thursday 26th
Classes from 12:00 to 16:00 (Central Time Zone)
DAY 6
Section 10: Modelling the variance as a time series process. Autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models. Stochastic volatility models.
Section 11: Continuous time models. Brownian motion and Ornstein-Uhlenbeck processes. Fitting continuous time series models using JAGS.
Section 12: Multivariate time series. Vector autoregression. Simple examples using JAGS.
GAMs and Bayesian models (Independant practical 5) please allow 3 hours to complete this before the next live session. This practical is not compulsory, you can complete this after the course if you do not have time.
Course Instructor
Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here
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