This is an advanced course in machine learning, time series and forecasting techniques. Students will apply advanced machine learning techniques such as xgboost, glmnet and random forests, as well as time series forecasting algorithms such as ARIMA and exponential smoothing, to real-world data such as financial prices and weather data. These techniques are used in financial trading, energy-demand forecasting, weather forecasting and other applications. The course will cover the main aspects of statistical and machine learning applications for forecasting.
- An overview of time series, including multivariate and hierarchical time series.
- Error measurement: an overview of forecasting error measurement techniques, common errors and better practice.
- Data preparation: pre-processing and de-trending time series for machine learning
- Hands-on exercises with real-world data in R
- Modelling with statistical time series analysis techniques
- Modelling with advanced machine learning techniques: glmnet, xgboost, random forest.
- Model evaluation and forward testing
Visualisation and analysis tools for time series analysis
- Path plots
- Out-of-time testing
- Model instability
- Feature engineering
- Multiple forecast horizons
- Predicting with uncertainty
- Measuring risk
- Explanatory variables
This is a masterclass, and as such may deviate from the planned agenda depending on the learning needs and level of the participants. Details of content may change at the instructor’s discretion. The course assumes a knowledge of R and a familiarity with basic machine learning and statistical concepts.
Time Series Forecasting with Machine Learning in R
I found the Introduction to R course extremely helpful. I have had very limited experience with R (and programming / statistical computing in general) and I now feel confident that I can use the language to do what I need with my data. The course was well designed and the notes are very helpful. I recommend this course to anyone who is new to R and wants to learn quickly.
—Helen McCormick, PhD student, Epigenetics Laboratory, Victor Chang Cardiac Research Institute
The Introduction to R course provided clear and logical assistance to getting up and running with R. More than that, the real value was in providing guidance on the myriad of online resources and introducing me to a network of passionate and helpful R users. Eugene is a knowledgeable and approachable teacher. I wouldn't hesitate in recommending the course. I feel that I am now fully on the road to applying R and using data to improve efficiency across my organisation.
—James Orton, Data and IT Manager, UNICEF Australia
I have been trying to convert my Stata programming skills to R, however, there have been many times where I just wanted to sit down with someone and have them explain the fundamentals of programming in R. Sure, a number of books and websites have helped me become familiar with R, however, I still didn't feel ready to translate all of my familiar Stata commands to R (e.g. I am comfortable plotting graphics using ggplot2, however, revert back to Stata for data manipulation). I knew that a more effective way to learn and feel confident would be to sit down with someone and have them explain how they use R, how they clean data, how they plot graphics, etc. I knew that once I felt comfortable with cleaning my data in R, analysis would be less of an issue— I'm happy to research the specifics on my own.
Thank you Eugene for advancing my R skills. I especially appreciate the time spent explaining the fundamentals of data manipulation — i.e. the code one needs to know before running any basic or sophisticated analysis. The pace of the workshop was perfect.
—Dr Chelsea Wise, Lecturer, Marketing, UTS Business School
Please ask about our discounts for group bookings.
Use firstname.lastname@example.org to email us any questions about the course, including requests for more detail, specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Course material may vary from what is advertised due to the demands and learning pace of attendees. Additional material may be presented along with or in place of what is advertised.
All bookings are made with the understanding that courses may be cancelled up to 5 working days before running, with all fees refunded.
Presciient training, coaching, mentoring, and capability development for analytics
Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice, and other services to build your organisation's strategic and operational analytics capability.
Our courses include:
- Introduction to R
- Predictive Modelling and Data Science for Big Data
- Forecasting and Trend Analysis
- Data Visualization
- Data Analytics for Fraud and Anomaly Detection in Forensics and Security
- Data Analytics for Campaign Marketing, Targeting and Insights
- Data Analytics for Insurance Claims analysis
- Data Analytics for Retail Marketing and Pricing
- Data Analytics for the Web
- Working with Data: Analysis and Report Writing for Everybody