Time Series Analysis and Forecasting with R - 4-week tutor-led online course

Description:

1. Course description.

The “Time Series Analysis and Forecasting with R” tutor-led 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.

A mixture of online pre-recorded instruction videos, weekly live group webinars with our tutor, additional 1-2-1 check-ins (either via email or on Microsoft Teams) and several homework exercises throughout the duration of the course will ensure you will be able to apply Machine Learning methods using R language to your own data and research questions.

 

2. Course structure and programme.

This instructor-led course is planned over four teaching weeks with an additional two-week follow-up period during which you will complete a small piece of work and receive a 1-2-1 feedback from our tutor. During the course, you will attend weekly live webinars (90 minutes each) with our tutors who will explain specific topics, answer your questions and discuss different statistical and machine learning concepts with R.

In between the four weekly online live tutorials (90 minutes long each) you will improve your skills by watching our pre-recorded instruction video tutorials at the Mind Project Learning Platform and working through set tasks (e.g. quizzes) as well as homework coding exercises which will require 6-8 hours of your time commitment per week. We estimate that the total time commitment is 30-38 hours over 4 teaching weeks.

During the course, you will also have weekly 1-2-1 check-ins (either via email or as a 15-minute Microsoft Teams call) with our tutor to supervise your progress and answer your questions.

This training course is tutor-led – online tutorials are presented live by our expert instructor, you can ask questions, discuss the topic and interact with other learners. You can also email the tutor during and after the course if you have any questions related to the material presented during the course.

The course will be recorded – you will have access to the pre-recorded video tutorials, recordings of the course live webinars and additional resources such as datasets, R code, academic papers and other publications related to the topics of the course, as well as essential and supplementary coding exercises via Mind Project Learning Platform.

Start date: Tuesday, 7th of November 2023, 10:00 am London (UK) time

Schedule of live sessions: Every Tuesday at 10:00 am London (UK) time for 4 weeks

Deadline for registrations: Friday, 3rd of November 2023 @ 17:00 London (UK) time

 

During this course, you will learn and implement a variety of data analysis and forecasting methods to explore and predict the patterns in time series data including linear trend, seasonal and exponential smoothing models, non-seasonal and seasonal ARIMAs, dynamic regression models as well as more advanced predictive analytics methods such as Support Vector Machines and Long Short-Term Memory approaches for multivariate time series forecasting. The course will be run according to the following schedule:

Week 1: Working with time series data in R

• 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,

• 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 2: 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,

• Using decomposition for forecasting,

• Evaluating forecasting accuracy,

• Simple forecasting approaches: naive model, average model, linear trend model.

Week 3: Univariate time series forecasting methods

• Introduction to univariate time series methods: simple exponential smoothing, Holt’s linear trend and Holt-Winter’s seasonal 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 4: 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.

Should you have any questions please contact Mind Project Ltd at info@mindproject.co.uk. Please visit the course website at Time Series Analysis and Forecasting with R - 4-Week Tutor-Led Training Course - November 2023.

Cost:

By 17th of October 2023 (Early Bird offer): £630 (normally £750) - Commercial fee - individual customers representing commercial/business entities, £480 (normally £600) - NGO/Gov/Academic fee - applicable to representatives of registered charitable organisations, national health service employees (e.g. NHS in the UK), employed academic staff (e.g. research assistants, lecturers, post-docs and above), and employees of governmental departments (e.g. civil servants), £330 (normally £450) - Student fee - applicable to undergraduate and postgraduate students only (confirmation of student status required). Additional discounts available for multiple bookings and groups.

Website and registration:

Register for this course

Region:

Greater London

Keywords:

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, R, Data Visualisation, Creating graphs and charts, Interactive data visualisation, ARIMA, dynamic models, Holt-Winters seasonal method, state-space models, Support Vector Machines, decision trees for time series

Related publications and presentations from our eprints archive:

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
R
Data Visualisation
Creating graphs and charts
Interactive data visualisation

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