Time Series Analysis and Forecasting with R - 6-week tutor-led online course
09/06/2022 - 14/07/2022
Mind Project Ltd
Simon Walkowiak MSc, MBPsS
Intermediate (some prior knowledge)
1. Course description.
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.
2. Course programme.
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 instruction videos via 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: Thursday, 9th of June 2022 @14:00 London (UK) time
Schedule of sessions: Every Thursday at 14:00 London (UK) time for 6 weeks
Deadline for registrations: Tuesday, 7th of June 2022 @ 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.
3. 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.
Should you have any questions please contact Mind Project Ltd at email@example.com or by phone on 0203 322 3786. Please visit the course website at https://www.mindproject.io/product/time-series-analysis-and-forecasting-with-r-6-week-tutor-led-online-course-june-2022/.
By 12th of May 2022 (Early Bird offer): £345 (normally £420) per person for the whole course (regular fee). £225 (normally £270) per person for the whole course applicable to undergraduate and postgraduate students, representatives of registered charitable organisations and NHS employees only (discounted fee). Additional discounts available for multiple bookings and groups.
Website and registration:
Statistical Theory and Methods of Inference, Regression Methods, Ordinary least squares (OLS), Generalized liner model (GLM), Linear regression, Time Series Analysis, Forecasting, Data Mining, Neural networks, Machine learning, Dynamic models, R, Data Visualisation
Related publications and presentations:
Statistical Theory and Methods of Inference
Ordinary least squares (OLS)
Generalized liner model (GLM)
Time Series Analysis