Machine Learning for Time Series (MLTP01)
Machine Learning for Time Series is a practical Python course teaching how to model, analyse, and forecast time series data using machine learning methods.
Duration: 5 Days, 7 hours per day
Next Date: May 11-15, 2026
Format: Recorded with a live Q & A
Ireland (GMT+1) local time - This is a hybrid course with recorded lectures and practicals and a live online Q and A session each day.
£450Registration Fee
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5.0
from 200+ reviews
- Overview
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Course Description
This course comprehensively introduces Machine Learning, covering theoretical foundations and practical applications in time series analysis. It focuses on crucial machine learning techniques, including supervised and unsupervised learning algorithms, utilising Python and popular libraries such as Scikit-learn, TensorFlow, and Keras. The course emphasises hands-on projects to apply learned concepts to real-world ecological time series problems.
What You’ll Learn
By the end of the course, participants should:
- Understand fundamental concepts in machine learning, including supervised and unsupervised learning.
- Be able to preprocess time series data for machine learning tasks.
- Understand key algorithms for regression, classification, clustering, and dimensionality reduction.
- Be familiar with model selection techniques and hyperparameter tuning in the context of time series analysis.
- Have confidence in deploying machine learning models using Python.
- Be able to apply machine learning techniques to solve real-world problems through hands-on projects involving time series problems.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of recorded lectures and hands-on practical exercises, with dedicated time for live Q and A sessions and for discussing participants’ own data, time permitting.
Global Accessibility
Recorded sessions are made available 30 days before the course start date allowing you to review the lectures and attempt the practicals ahead of time.
Collaborative Discussions
Open discussion sessions during the Q and A sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is ideal for researchers—including PhD and MSc students, postdoctoral researchers, and principal investigators—as well as environmental professionals interested in applying best practices and state-of-the-art machine learning methods for modelling time series data; to make the most of the course, some prior experience with Python is recommended, and while key concepts will be reviewed on the first day, a basic familiarity with the language is advisable.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of Python is required. Python is a free, open-source software compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
All necessary Python packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Mark Andrews
Mark is a psychologist and statistician whose work lies at the intersection of cognitive science, Bayesian data analysis, and applied statistics. His research focuses on developing and testing Bayesian models of human cognition, with a particular emphasis on language processing and memory. He also works extensively on the theory and application of Bayesian statistical methods in the social and behavioural sciences, bridging methodological advances with real-world research challenges.
Since 2015, Mark has co-led a programme of intensive workshops on Bayesian data analysis for social scientists, funded by the UK’s Economic and Social Research Council (ESRC). These workshops have trained hundreds of researchers in the practical application of Bayesian methods, particularly through R and modern statistical packages.
Education & Career
• PhD in Psychology, Cornell University, New York (Cognitive Science, Bayesian Models of Cognition)
• MA in Psychology, Cornell University, New York
• BA (Hons) in Psychology, National University of Ireland
• Senior Lecturer in Psychology, Nottingham Trent University, England
Research Focus
Mark’s work centres on:
• Bayesian models of human cognition, especially in language processing and memory
• General Bayesian data analysis methods for the social and behavioural sciences
• Comparative studies of Bayesian vs. classical approaches to inference and model comparison
• Promoting reproducibility and transparent statistical practice in psychological research
Current Projects
• Developing Bayesian cognitive models of memory and linguistic comprehension
• Exploring Bayesian approaches to regression, multilevel, and mixed-effects models in psychology and social science research
• Co-leading ESRC-funded workshops on Bayesian data analysis for applied researchers
Professional Consultancy & Teaching
Mark provides expert training and advice in Bayesian data analysis for academic and applied research projects. His teaching portfolio includes courses and workshops on:
• Bayesian linear and generalized linear models
• Multilevel and mixed-effects models
• Cognitive modelling with Bayesian methods
• Applied statistics in R for psychologists and social scientists
He is also an advocate of open science and is experienced in communicating complex statistical methods to diverse audiences.
Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels
Links
• University Profile
• Personal Page
• ResearchGate
This day provides participants with the foundational R skills required for working with time series and machine learning. It is designed for beginners or those needing a refresher in R programming.
Session 1 – 03:00:00 – (Python Essentials for Time Series:
This section focuses on Python syntax, variables, data types, conditionals (`if`, `else`, `elif`), loops (`for`, `while`), and writing reusable code using functions.
Session 2 – 03:00:00 – Data Structures and File Handling in Python:
Focuses on lists, dictionaries, and tuples, sets, and reading/writing files (e.g., CSVs) for data manipulation. (3:00:00)
Session 1 and 2 live Q and A from 19:00 – 20:30
This day focuses on the theoretical foundations of time series analysis, detailing the main aspects.
Session 3 03:00:00 – The Time Series Setting:
This section covers the fundamental structure of time series data, basic time series handling techniques, and an introduction to various time series operations, such as differentiation, decomposition, and others.
Session 4 – 03:00:00 – Classical Models in Time Series Analysis I
This section examines classical time series analysis models, including Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA) models, and their variants with exogenous time series.
Session 3 and 4 live Q and A from 19:00 – 20:30
This day focuses on the practical and theoretical foundations of more advanced methods of time series analysis:
Session 5 – 03:00:00 – Classical Models in Time Series Analysis II:
Introduces more advanced models, such as Vector Autoregressive (VAR) Models, Seasonal Autoregressive Integrated Moving Average (SARIMA), Autoregressive Conditional Heteroskedasticity (ARCH), and Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models.
Session 6 – 03:00:00 – Time Series Reconstruction and Decomposition:
Provides a detailed explanation of more advanced methods used for time series reconstruction and decomposition and their connection with ML.
Session 5 and 6 live Q and A from 19:00 – 20:30
This day focuses on the theoretical foundations of machine learning, detailing the application of learning algorithms and preparing for the practical examples in Python.
Session 7 – 03:00:00 – Introduction to Machine Learning:
This section covers the definition of Machine learning, types of Learning (Supervised, Unsupervised, Reinforcement, Semi-Supervised), applications of Machine Learning to time series, and an overview of Python libraries for ML (NumPy, scikit-learn).
Session 8 – 03:00:00 – Fundamental Learning Algorithms:
This section examines the available learning algorithms and their applications. We will also discuss the application of various algorithms, accompanied by practical examples in Ecology.
Session 7 and 8 live Q and A from 19:00 – 20:30
Participants will learn about the end-to-end workflow for a typical project involving time-series analysis and machine learning.
Session 9 – 03:00:00 – Preprocessing Data and Selecting Algorithms:
This section focuses on preprocessing techniques in NumPy and scikit-learn to prepare time series data for machine learning models. An entomological example illustrating the application of Machine Learning to ecology will be used.
Session 10 03:00:00 – The Complete Machine Learning Pipeline for Time Series:
Covers the end-to-end machine learning workflow, including the use of preprocessed series data and the creation of scikit-learn pipelines to automate critical aspects of the workflow.
Session 9 and 10 live Q and A from 19:00 – 20:30
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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