Tidy time series & forecasting in R

About AFRICAST project

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Course Overview

Forecasting is a valuable tool that allows organizations to make informed decisions about the future. Time series forecasting, in particular, uses historical data to predict future trends over time. This technique has extensive applications across a wide range of fields, including finance and economics, health and humanitarian operations, supply chain management, and more. By analyzing trends and patterns in data, time series forecasting can help decision-makers identify potential challenges and opportunities, and plan accordingly.

It is important for researchers in Low- and Middle-Income Countries (LMICs) to develop technical skill in data analysis and forecasting techniques, which are essential for accurate and reliable forecasting. By having these skills, researchers can analyze data, identify trends and patterns, and develop robust forecasting models to make informed decisions that can improve resource allocation and planning in LMICs. Additionally, researchers can collaborate with policy makers and stakeholders to ensure that the forecast results are integrated into decision-making processes, leading to more efficient and effective resource management strategies.

This workshop is part of the Forecasting for Social Good (F4SG) initiative, and will run online from the 10th-14th November 2025.

Learning objectives

During the training, participants will gain knowledge and skills in:

  1. Preparing time series data for analysis and exploration.
  2. Extracting and computing useful features from time series data and effectively visualizing it.
  3. Identifying appropriate forecasting algorithms for time series and selecting the best approach for the data at hand.

Educators

Instructor

Headshot of Mitchell O'Hara-Wild Mitchell O’Hara-Wild (he/him) is a PhD candidate at Monash University, creating new techniques and tools for forecasting large collections of time series with Rob Hyndman and George Athanasopoulos. He is the lead developer of the tidy time-series forecasting tools fable and feasts, and has co-developed the widely used forecast package since 2015. Mitchell also operates a data consultancy, and has worked on many forecasting projects that have supported decision making and planning for businesses and governments. He is an award-winning educator, and has taught applied forecasting at Monash University and various forecasting workshops around the world.

Blog  Github  LinkedIn  

Instructor

Headshot of Dr. Bahman Rostami-Tabar Bahman is the Professor of Data-Driven Decision Science at Cardiff Business School, Cardiff University, UK. He is interested in transforming data into insights for making smart decisions. Bahman is the founder and director of Data Lab for Social Good Research Group at Cardiff University and the founder of Forecasting for Social Good committee initiatives sponsored by the International Institute of Forecasters. He is also leading the “Uncertainty & the Future” theme at the Digital Transformation Innovation Institute.

Bahman specializes in the development and application of modelling, forecasting and management science tools and techniques providing informed insights for planning & decision-making processes in sectors contributing to social good, including healthcare operations, global health and humanitarian supply chains. His collaborative efforts have spanned a multitude of organisations, including notable bodies such as the National Health Service (NHS), Welsh Ambulance Service Trusts (WAST), United States Agency for International Developments (USAID), the International Committee of the Red Cross (ICRC), and John Snow Inc. (JSI). A remarkable highlight of his contributions is his pivotal role in disseminating forecasting knowledge especially in low and lower-middle income countries through the democratizing forecasting project sponsored by International Institute of Forecasters.

Website  Github  X 

Instructor

Headshot of Debra Okeh Debra Okeh is a Cohort 1 Fellow of the Nigeria Malaria Modelling Fellowship, CMS, Nigeria. She also serves as a member of the Learning Committee of the Applied Malaria Modelling Network (AMMnet) and the Assistant General Secretary of AMMnet Nigeria. As a global health physician and infectious disease epidemiologist, she specializes in translating complex epidemiological models into actionable policy. Her research interests are on using modelling for strategic planning by utilising subnational data to predict the impact of malaria control interventions, and implementing time-series analysis for disease forecasting. She has honed her skills in providing modelling and analytical support to the Nigeria National Malaria Elimination Programme. Her expertise has been recognised through presentations at Harvard’s Centre for Communicable Disease Dynamics, the 2024 American Society of Tropical Medicine and Hygiene Conference, the Gates Foundation 2024 Institute for Disease Modeling Symposium, and the 2024 Multilateral Initiative on Malaria (MIM) Society 8th Pan-African Conference.

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Mentors for the cohort 2025

A committed team, comprising both PhD students and MSc. students, generously dedicates their time and expertise to offer valuable support to learners throughout the duration of the workshop to help learners with the exercises.

The team includes:

  • Mama Kouandé Abdou Rakib
  • Ninguin Romuald Guillaume
  • Sode Akoeugnigan Idelphonse
  • Akounda Badibassa
  • Mihertab Ermias
  • Agnes Njambi
  • Caroline Mugo
  • Catherine Chepkoech Munga
  • Charity Wamwea
  • Dickson Ochichi Suguku
  • Herbert Imbogah
  • Matabel Odin
  • Simon Kamau
  • Susan Mwelu
  • Abiodun Egwuenu
  • Adamu Saidu Saleh
  • Adebayo, Olatunji Joyce
  • Anthony C. Uwandu-Uzoma
  • Haruna Jimoh
  • Hassan Adam Murtala
  • Okorie, Oluchukwu Chiamaka
  • Olagbami Oluwafemi Samson
  • Ugonma Regina Okorie
  • Shittu Ola Abdulganiy
  • Ali Mohamed Ali
  • Kundu Sam
  • Ssembatya Ronald

Project coordination

The coordination and administration of the project are overseen by a dedicated team from Jomo Kenyatta University of Agriculture and Technology.

Jomo Kenyatta University team includes:

  • Henry Kissinger Ochieng
  • Caroline Mugo
  • Winnie Chacha
  • Samuel Mwalili

Preparation

The workshop will provide a quick-start overview of exploring time series data and producing forecasts. There is no need for prior experience in time series to get the most out of this workshop.

It is expected that you are comfortable with writing R cod and using tidyverse packages including dplyr and ggplot2. If you are unfamiliar with writing R code or using the tidyverse, consider working through the learnr materials here: https://learnr.numbat.space/.

Some familiarity with statistical concepts such as the mean, variance, quantiles, normal distribution, and regression would be helpful to better understand the forecasts, although this is not strictly necessary.

Required equipment

Please have your own laptop capable of running R.

Required software

To be able to complete the exercises of this workshop, please install a suitable IDE (such as RStudio), a recent version of R (4.1+) and the following packages.

  • Time series packages and extensions
    • fpp3, sugrrants
  • tidyverse packages and friends
    • tidyverse, fpp3

The following code will install the main packages needed for the workshop.

install.packages(c("tidyverse","fpp3", "GGally", "sugrrants", "astsa", "usethis"))

After installing these packages, you can create download the exercises and data for the course using the following code:

usethis::use_course("https://workshop.f4sg.org/africast/exercises.zip")

Please have the required software installed and pre-work completed before attending the workshop.