My life objective is to help the World becoming a better place by offering skills and developing free resources for the population that may not have the means to afford a quality education in forecasting and modelling. That was the force behind Democratising Forecasting project that I initiated in January 2018.
Democratising forecasting project
The initiative sponsored by the International Institute of Forecasters provides cutting-edge training in the use of forecasting with R software in developing countries. There is no doubt that many people in developing countries cannot afford high fees to attend forecasting workshops. The project aims to make such a training accessible to those people and provide up-to-date training on the principles of forecasting and create a network of forecasters to conduct research on forecasting with social impact for less developed countries. The training has been delivered in Tunisia, Iraq, Senegal, Uganda, Nigeria, Turkey, Indonesia so far.
This year I decided to expand the initiative to cover organisations with social missions such as National Health Service (NHS), UK and Humanitarian Organisations such as Red Cross and United Nations, Switzerland, given their missions on improving human-wellbeing. We give our time for free despite the debate that such organisations may afford to pay such trainings. We organise four workshops across the UK every year.
Dr. Bahman Rostami-Tabar is the main instructor for the workshops. Colleagues from other Universities in the UK may join him to deliver trainings. Bahman is a Senior Lecturer (Associate Professor) in management science at Cardiff Business School, Cardiff University, UK. He is interested in the use and the implication of forecasting in social good areas such as health and humanitarian. He has been involved in various forecasting related projects with NHS Wales, Welsh Ambulance Service Trust and the International Committee of the Red Cross. He is currently working on projects that focus on developing innovative forecasting methodologies and their links to decision making in health and humanitarian operations.
Why forecasting for NHS
Ensuring life-saving services are delivered to those who need them require far more than money, infrastructure and scientific progress. Accurate modelling and forecasting systems can assist critical decisions that drive policy making, funding, research, and development in the NHS. Decision makers are making decisions every day with or without forecasts. However, they are more robust and well-informed in the light of what could happen in the future, and that is where forecasting becomes crucial. However, implementing forecasting principles to support decision-making process requires significant technical expertise. To that end, we aim to organize a two-day workshop on forecasting to teach participants how to apply principles of accurate forecasting using real data in healthcare.
Workshop dates for 2020
We are offering four workshops in 2020 as following
- Monday 24th & Tuesday 25th February 2020
- Wednesday 27th & Thursday 28th May 2020
- Wednesday 16th & Thursday 17th September 2020
- Wednesday 13th & Thursday 14th October 2020
Who should attend
This workshop is for you if you are:
- A decision maker who wants to use forecasting tools and techniques using R to empower decision making;
- A data analyst(data scientist) who wants to gain in depth understanding of forecasting process;
- A Forecaster who wants to learn how to use R software for forecasting purpose.
What participants will learn in the workshop
Assuming basic knowledge of statistics, participants will be able to do the following tasks when using forecasting to support decision making process in real World:
- Determine what to forecast according to a forecasting process framework;
- Prepare and manipulate data using functions in basic R, tidyverse and lubridate packages in R;
- Identify systematic patterns using time series toolbox in ggplot2 and forecasting related packages in R such as forecast package;
- Produce point forecasts and prediction intervals using R functions in forecasting related packages such as forecast and fable and user defined functions;
- Determine the accuracy of forecasting models using statistical accuracy performance measures for point and prediction intervals;
- Visualize, export and report result for interpretation and insights using RMarkdown.
Basic knowledge in statistics is assumed, e.g. “I know what normal distribution is”;
Basic knowledge of R is assumed, e.g. “I know what data type and data structure is”, “I know how to use a function”;
No knowledge of forecasting is assumed;
Title: Essentials to do forecasting using R
Date: Two weeks before the workshop (TBC)
1.1. Forecasting and decision making: What is forecasting? How forecasting task is different from other modelling tasks? What is the link between forecasting and decision making, how to identify what to forecast?
1.2. Data preparation, and manipulation: how to prepare data for the forecasting task, how to clean data? How to manipulate data to extract time series?
1.3. Time series patterns and decomposition: what could be used in data for forecasting task? how to detect systematic pattern in the data? how to separate non-systematic pattern?
1.4. Forecaster’s toolbox: How to use time series graphics to identify patterns? what is a forecasting benchmark? What are the simple forecasting methods that could be used as benchmark? How to generate point forecasts and prediction interval using simple forecasting methods?
1.5. Forecast accuracy evaluation: How do we know if the forecasting method captures systematic patterns available in data? How to judge whether a forecast is accurate or not? How to evaluate the accuracy of point forecasts and prediction interval? Why do we need to distinguish between fitting and forecast?
1.6. Exponential smoothing models: What is the exponential smoothing family? what are available models in this family? What is captured by this family? how to generate point forecast and prediction intervals using exponential smoothing models?
2.1. ARIMA models: This is another important family of forecasting models. What is the ARIMA framework? what are available models in this family? What is captured by this family? how to generate point forecast and prediction intervals using ARIMA models?
2.2. Regression: We also look at causal techniques that consider external variables. What is the difference between regression and exponential smoothing and ARIMA? How to build a simple regression model?
2.3. Specie events: In addition to using systematic patterns in time series, we discuss how to include deterministic future special events, such as holidays, festive days, etc in models.
2.4. Forecasting by aggregation: How to forecast in a situation where we have a high frequency time series, e.g. daily but we need a low frequency forecast, e.g. monthly? How do we generate forecast for items with a hierarchical or grouped time series structure?
2.5. Forecasting by combination: how to use an ensemble of forecasting approaches? In which conditions ensemble forecasts are better than individual methods?
2.6. Forecasting for many time series: what is the best approach to forecast many time series? How to classify items for forecasting? How to forecast them? How to report the accuracy?
Start: 09:30 a.m. End: 04:30 p.m.
- Morning: 11:00 - 11:20
- Afternoon: 03:00 – 03:20 p.m.
- Lunch: 12:30 p.m. – 01:30 p.m.