Time Series Analysis and Forecasting with R – Tutor-Led Online Course

Start date: Wednesday, 9th of September 2020, 14:30 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 Wednesday at 14:30 London (UK) time for 6 weeks
Recommended time commitment: 6-8 hrs per week
Maximum number of attendees: 15 learners
Deadline for registrations: Monday, 7th of September 2020 at 17:00 London (UK) time

Book your place on this course by 19th of August 2020 to be eligible for a lower Early Bird price.

This product is currently out of stock and unavailable.


This course is now fully-booked. We are currently working on releasing dates for new training courses in Winter 2020/2021 and Spring 2021. Please contact us should you wish to pre-book your place or arrange this course at your organisation. 

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.


Course benefits

  • 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 (up to 15 learners) 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 additional 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 which can be upgraded to a course completion certificate upon successful submission of a short data analysis report (up to 2,000 words) and R code scripts showing all your workings,

  • You will be encouraged to network with other learners enrolled on the course – you will automatically become a member of the course forum/message board where you can ask general course questions and you will maintain your learner’s profile in which you can share your bio and social media links.

Course schedule

This instructor-led course duration is planned over 6 teaching weeks (to qualify for the Course Attendance Certificate) plus an additional 1 calendar month for the completion of the data science project (to obtain the graded Course Completion Certificate).

In between the six weekly online live tutorials (2.5 hours long each) you will improve your skills working through set tasks and homework exercises which will require 4-6 hours of your time commitment per week (24-36 hours). We estimate that the total time commitment for the Course Attendance Certificate is 40-50 hours over 6 teaching weeks, and for the Course Completion Certificate it will equate to 70-80 hours (over 2.5-month period) including the project report writing time.

Start date: Wednesday, 9th of September 2020 @14:30 London (UK) time

Schedule of sessions: Every Wednesday at 14:30 London (UK) time for 6 weeks

Deadline for registrations: Monday, 7th of September 2020 @ 17:00 London (UK) time


Week 1: Working with time series data in R – Part 1

  • Challenges with time series data with R,
  • Importing time series data,
  • Converting between different time series objects,
  • Extracting specific components of data and time.

Week 2: Working with time series data in R – Part 2

  • Plotting time series data with ggplot2,
  • Downsampling and upsampling time series,
  • Handling time series missing values,
  • Exploratory analysis of time series data,
  • Building on exploratory analysis of time series: moving averages, lagged values and rolling statistics.

Week 3: Time series analysis with R

  • Time series decomposition methods,
  • Autocorrelation, stationarity and differencing,
  • Transformations and adjustments,
  • Using decomposition for forecasting,
  • Evaluating forecasting accuracy.

Week 4: Introduction to time series forecasting methods with R

  • 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.

Additionally, in order to receive the full Course Completion Certificate, you will have to submit a short data analysis report (up to 2,000 words) along with R data processing and analysis scripts within one calendar month from the last day of Week 6. The project will be assessed and graded. You will also receive a formal written feedback about your project.


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). As R is a free and open-source environment you can download it directly from https://cloud.r-project.org/ website and RStudio Desktop is available at https://rstudio.com/products/rstudio/download/. Please contact us should you have any questions or issues with the installation process. No specific R packages are required before the course (the course tutors will explain this during the training).

  • 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) during the tutor-led video sessions – please note we use open-source Jitsi video conferencing application directly deployed on our secure server (located in Ireland, European Union, and provided by Microsoft).

  • You will need at least one commonly used web browser installed on your PC (e.g. Chrome, Safari, Firefox, Edge etc.) in order to attend the video-streamed tutorials. You may also use your mobile phone (Android or iOS) to connect to our tutor-led video sessions, in that case please install Jitsi Meet Mobile available at https://jitsi.org/downloads/. Meeting ID, along with personal usernames and passwords will be provided to the registered learners before the course.

  • The primary spoken and written language of the course is English.

  • You are encouraged to complete the online Learner’s Skills Inventory to allow Mind Project and our course tutors to customise the course teaching style depending on the level of attendees’ knowledge and their areas of interest. The data obtained through the Learner’s Skills Inventory will be held fully-confidential and will only be used to provide a quality statistical computing and data science training.

  • 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.

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.

Time Series Analysis and Forecasting with R – Tutor-Led Online Course