Time Series Forecasting with R – Online Training Course


When? 10th-11th of June 2019

Where? London Institute of Banking & Finance, 8th Floor, Peninsular House, 36 Monument Street, London, EC3R 8LJ, United Kingdom

Course type? 2-day open-to-public training course

Deadline for registrations? Thursday, 6th of June 2019 at 16:00 London (UK) time

Book your place on this course by 24th of May 2019 to be eligible for a lower Early Bird price.



The “Time Series 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. Regression Trees, Support Vector Machines and Long-Short Term Memory deep learning methods.


Who is this course for?

This online course is suitable for all data scientists, researchers and business analysts interested in time series forecasting methods using R programming language. Specifically, this course is 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 programme outline

This unique online training course consists of a mixture of short lecture-style presentations, practical tutorials and exercises which will help you to internalise all learnt skills and apply them to solve specific time series forecasting problems. The example datasets used during tutorial and exercise sessions come from social sciences, business and finance fields. All datasets used in this course are open-access data collections.

The programme for this online course covers the following concepts and topics:

  • Import, clean and pre-process time series data using standard R functionalities and its third-party libraries e.g. tidyverse family of packages (dplyr, tidyr etc.),

  • Manipulate time series data structures including their indexing, subsetting and slicing,

  • Convert date/time stamps into varying date/time units, convert between time series frequencies using different resampling methods and dealing with missing values,

  • Carry out time series data aggregations using pivot tables, cross tabulations and data summaries,

  • Decompose and visualise all components of time series data (trend, seasonality, residuals, etc.),

  • Calculate moving/rolling averages and other rolling single-value statistics, lagged and shifted time series, percentage changes between data points of different time series frequencies,

  • Assess stationarity of time series and perform varying methods of differencing,

  • Predict future data using simple linear trend and multiple regression models for time series data including methods of measuring model accuracy and model diagnostics,

  • Estimate parameters of non-linear or locally-weighted models, regression trees and random walk models,

  • Perform more advanced forecasting methods using Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models,

  • Measure the ARIMA model accuracy using various accuracy metrics, compare and select the models.

Course pre-requisites

Further instructions

  • In order to benefit from the course, 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 www.r-project.org website and RStudio is available at https://www.rstudio.com/products/rstudio/#Desktop. A list of specific R packages to install is available above in the “Course pre-requisites” section.

  • 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 training course.

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

  • By enrolling on one of our online training courses you agree to the Online Training Terms and Conditions. Please read the Online Training Terms and Conditions before purchasing this course.

Discounts and multiple bookings

Apart from discounted fees for students or employees of charitable organisations, 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.

Please note that the 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 you at our office or your own premises at a discounted rate.


Arrange this course at your premises

This open-to-public course is a shortened and more general version of our fully-customisable in-house training course “Time Series Analysis with R”. If your delegates cannot attend this public course, or you are interested in arranging this training course at your premises or simply you need a bespoke, made-to-measure training solution, please visit this page and press Ask For Quote button to enquire about and request a quote for the in-house version of this course based on your specific needs and desired outcomes of the training.

You may also 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 days (or half-days) you would like to arrange the course for (including additional support/project guidance if needed),

  • location of the training venue,

  • 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.

Time Series Forecasting with R – Online Training Course