Time Series Analysis and Forecasting with R – 6-Week Tutor-Led Online Course – November 2022
Start date: Friday, 4th of November 2022, 14:00 London (UK) time
Course type: 6-week tutor-led online course with certification
Course duration: 2.5 hrs tutor-led virtual lessons per week for 6 weeks
Schedule of sessions: Every Friday at 14:00 London (UK) time for 6 weeks
Recommended time commitment: 6-8 hrs per week
Deadline for registrations: Wednesday, 2nd of November 2022 at 17:00 London (UK) time
Book your place on this course by 14th of October 2022 to be eligible for the Early Bird discount.
The “Time Series Analysis and Forecasting with R” online training course will provide you with essential knowledge to allow wrangling, processing, analysis and forecasting of time series data using specialised libraries such as ts, xts, zoo, tsibble, prophet, fable and forecast for R programming language. Whether you wish to analyse financial data, predict sales or marketing revenue, or understand temporal patterns in your social, medical or economic data, this course will provide you with theoretical and practical understanding on how to clean, visualise and model time series data in your workflows using R programming language.
During the course, you will first learn to manipulate the imported data, extract necessary date/time stamps and transform the processed data into supported time series R objects. You will then proceed to perform essential time series exploratory and decomposition operations, calculate selected moving/rolling single-value statistics, convert between differing time frequencies, visualise and prepare data for predictions. The forecasting part will include sessions on estimating linear, non-linear and locally-weighted trends, multiple regression models, ARMA and ARIMA approaches, dynamic models and a selection of machine learning and AI methods applicable to time series data e.g. Support Vector Machines and Long-Short Term Memory deep learning methods.
Who is this course for?
This tutor-led online course is suitable for all data scientists, researchers and business analysts interested in time series forecasting methods using R programming language. This course is also recommended for undergraduate and postgraduate students in social sciences, data science, economy, finance/banking, public health and medical science, and all other fields which implement time series or longitudinal methods.
This training course is tutor-led – all online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners,
In total, this course includes 15 hours of live teaching which equates to a 3-day classroom-based training course with exclusion of tea/coffee and lunch breaks,
Exercises and tasks completed by you between the live tutorial sessions will help you in better material retention and will enhance your learning progress,
Small group size allows easy interaction and stimulating environment for successful learning,
Email supervision and support provided during the course period maximises learning outcomes and improves your learning experience,
You will have access to self-paced online learning materials e.g. tutorial videos, exercises and quizzes, R code scripts used during tutorials, example datasets, optional and mandatory reading (e.g. blog articles, academic papers, industry reports), and other external recommended resources e.g. online books,
You will receive a course attendance certificate upon successful submission R coding exercises and quizzes.
Your course instructor
Your instructor for this course will be Simon Walkowiak. Simon is a director at Mind Project Limited and a Ph.D. researcher in Artificial Intelligence at the Bartlett Centre for Advanced Spatial Analysis (University College London) and the Alan Turing Institute in London. Simon holds BSc (First Class Honours) in Psychology with Neuroscience and MSc (Distinction) in Big Data Science. He conducts and manages research projects on implementation and computational optimisation of novel AI approaches applicable to large-scale datasets to predict human behaviour and spatial cognition. Simon is the author of “Big Data Analytics with R” (2016) – a widely used textbook on high-performance computing with R language and its compatibility with ecosystem of Big Data tools e.g. SQL/NoSQL databases, Spark, Hadoop etc. Apart from research and data management consultancy, during the past several years, Simon has taught at more than 150 in-house or open-to-public statistical training courses (in R, Python, SQL and Scala for Spark languages) in the UK, Europe, Asia and USA. His major clients include organisations from finance and banking (HSBC, RBS, GE Capital, European Central Bank, Credit Suisse, ING etc.), research and academia (GSMA, CERN, University of Cambridge, UK Data Archive, Agri-Food Biosciences Institute, Newcastle University etc.), health (NHS), insurance (Liberty IT), transport (Steer Group) and government (Home Office, Ministry of Justice, Government Actuary’s Department etc.).
This instructor-led course duration is planned over 6 teaching weeks.
In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills by watching pre-recorded video tutorials at our Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment is 40-50 hours over 6 teaching weeks.
Start date: Friday, 4th of November 2022 @14:00 London (UK) time
Schedule of sessions: Every Friday at 14:00 London (UK) time for 6 weeks
Deadline for registrations: Wednesday, 2nd of November 2022 @ 17:00 London (UK) time
Week 1: Working with time series data in R – Part 1
- Challenges with time series data with R,
- Creating time series objects and data structures,
- Converting between different time series objects,
- Importing time series data,
- Pre-processing time series data,
- Converting between date/time types.
Week 2: Working with time series data in R – Part 2
- Extracting specific components of data and time,
- Plotting time series data with ggplot2 and interactive visualisation packages e.g. plotly and highcharter,
- Downsampling and upsampling time series,
- Handling time series missing values,
- Exploratory analysis of time series data.
Week 3: Time series analysis with R
- Building on exploratory analysis of time series: moving averages, lagged values and rolling statistics,
- Time series decomposition methods,
- Autocorrelation, stationarity and differencing,
- Transformations and adjustments.
Week 4: Introduction to time series forecasting methods with R
- Using decomposition for forecasting,
- Evaluating forecasting accuracy,
- Simple forecasting approaches: naive model, average model, linear trend model,
- Introduction to univariate time series methods: simple exponential smoothing, Holt’s linear trend and Holt-Winter’s seasonal methods.
Week 5: Univariate time series forecasting methods
- Exponential smoothing state space models,
- Autoregressive (AR) and moving average (MA) models, non-seasonal and seasonal ARIMAs,
- Facebook’s prophet library for univariate time series forecasting,
- Introduction to deep learning for univariate time series with Long Short-Term Memory (LSTM).
Week 6: Multivariate time series forecasting methods (and AI)
- Multiple linear regression with time series data,
- Polynomial regressions with time series data,
- Combining ARIMAs with multiple linear regressions: dynamic regression models,
- Support vector machines (SVMs) and kernel smoothing methods with multivariate time series,
- Long Short-Term Memory for multivariate time series forecasting.
Course pre-requisites and further instructions
We recommend that you have the most recent version of R and R Studio software installed on your PC (any operating system). R is a free and open-source environment and you can download it directly from https://cloud.r-project.org/ website. RStudio Desktop (also free) is available at https://rstudio.com/products/rstudio/download/. Please contact us should you have any questions or issues with the installation process. A list of R packages to pre-install before the course will be sent to the enrolled attendees in the Welcome Pack alongside other Joining Instructions.
We recommend that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. We suggest that the course is preceded with our “Applied Data Science with R” open-to-public tutor-led online training course.
Your PC needs to be connected to a stable WiFi/Internet network (either home or office-based) and have Zoom video-conferencing application installed.
You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) to access our Mind Project Learning Platform.
The primary spoken and written language of the course is English.
By enrolling on one of our online training courses you agree to the Training Terms and Conditions. Please read the Training Terms and Conditions before purchasing this course. Please note we reserve the right to cancel the course in case the number of registered attendees on the course is less than 5 individuals one week before its scheduled start date.
Discounts and multiple bookings
Early Bird Offer allows you to save up to 15% off the total course enrolment price (on top of other discounts). This offer is usually valid up until 3-4 weeks before the start of the course.
We offer 2 types of enrolment options:
- Regular Fee – full-priced enrolment for learners representing commercial organisations or self-funded individuals who do not meet our eligibility criteria for discounted rates (please see below),
- Discounted Fee – applicable to undergraduate and postgraduate students as well as representatives of registered charitable organisations and non-governmental organisations (NGOs) – this category also includes employees of the National Health Service (NHS).
Students and individuals eligible for the Discounted Fee should submit a copy of their student or organisation ID card (with their name and card expiry date visible) when making the purchase of their place on the course for the discount eligibility verification purposes. Alternatively, the discount eligibility can be verified by submitting either i.) a copy of a letter from the university registrar or student’s department confirming your status, or ii.) a copy of a letter from your employer (on a company letter-headed paper with a charity/NGO registration number) which confirms your current position within the organisation.
Apart from the Early Bird Offer and discounted fees for students or employees of charitable organisations and NGOs, we are able to offer further discounts on the overall cost of your training if you wish to attend multiple related courses or enrol several delegates on this specific course. Please note that this offer is only available through our website.
- If you book 3 or 4 tickets on any of our tutor-led open-to-public online training courses, you will receive 5% discount on the total price of your booking.
- If you book 5 or more tickets on any of our tutor-led open-to-public online training courses, you will receive 10% discount on the total price of your booking.
All discounts are calculated automatically when tickets are added to the Cart. For bookings of 6 and more delegates on one course, we recommend that you contact us directly – we may be able to arrange a separate course just for your delegates at a discounted rate.
Arrange this course at your organisation
This open-to-public online course is a more generalised version of our fully-customisable in-house / online training course “Time Series Analysis and Forecasting with R”. If your delegates cannot attend this public course, or you are interested in arranging this training course explicitly for your delegates (or at your premises) or simply you need a bespoke, made-to-measure training solution, please request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.
You may email us directly at info(at)mindproject.io and include the following information in your enquiry:
contact details to a person who should receive the quote,
number of delegates you would like to train,
approximate number of online sessions (or half-days / full days for on-site in-house course) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue if not online,
any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,
any other questions you may have.
If you don’t know the answers to questions above or you are at early stages of course planning, we would be happy to arrange an informal chat and help you choose the most suitable and budget-efficient option.