Professional and short courses
|Location||Cavendish, Central London|
|Faculty||Science and Technology|
Planning for both the short- and long-term future in health and social care is vital. Allocating too many resources to a service can lead to waste and overspend, while under provision can lead to long waiting times and even the potential for loss of live. Future estimates of demand for a service are also useful in the designing of procurement contracts and logistical plans.
The advanced forecasting module builds on the concepts and core ideas introduced in the introductory forecasting course, while providing attendees with specialist knowledge of more rigorous forecasting techniques.
In particular, the module will cover ARIMA models, the Box-Jenkins approach and various ensemble forecasting approaches to aid the health or social care planner produce more reliable forecasts in dynamically changing environments. Although it is not strictly necessary to have attended the introductory course, it is highly recommended that you have done so as this will enable you to get the best from the material covered.
The course is delivered in a face-to-face format using lectures and course materials to introduce and explain the various theoretical concepts. Later in the day, an afternoon session held in the computer labs will provide you with a chance to practice what you have learnt to industrial problems.
Who is the course for?
The course is ideally suited to those who want to increase their understanding of forecasting methods and best practices, in particular when applied to problems arising in local health or government services. Compared with other courses in the field we go that extra mile and do not just teach you which combinations of boxes to tick in software packages. The course will therefore most interest those looking to deepen their knowledge of advanced forecasting techniques and their limitations.
Participants are encouraged to take this course in combination with the introductory course, otherwise a numerical background in finance, accounting, budgeting or purchasing will be more than sufficient for the majority of the material.
|Courses no longer available|
|Start date||Duration||Day and time||Price||Apply|
|1 June 2015||1 day||TBA||£350||Already started|
- introduction to advanced forecasting
- principles of model design
- data collection and model fitting
Time series analysis
- ARIMA modelling
- Box-Jenkins approach
- interpreting PACF and ACF
Ensemble forecasting and chaotic systems
- combining results of multiple models
- simulation models using Monte Carlo methods
Demonstration and workshop where attendees will be able to work on problems using methods taught during the first part of the day. We will introduce how to use such techniques in both SPSS and @RISK for Microsoft Excel.
At the end of this module you will:
- have increased your awareness of the steps involved in the forecasting process
- appreciate the role of forecasting methods in strategic planning
- understand and know which type of technique to select in different forecasting domains
- demonstrate the ability to use able to use the ARIMA modelling framework to create time series forecasts
- be able to apply the newly learnt approaches using a suitable statistical package, such as SPSS
- have practical experience of using the latest version of SPSS (IBM version 20) and @RISK for Microsoft Excel
Philip Worrall obtained a BA (Econ) in Economics from the University of Manchester and an Operational Research and Management Science MSc from Lancaster University. His MSc project was conducted at the NHS Derbyshire County Primary Care Trust, where he investigated the challenges of building COPD strategic planning models to model long-term future demand. At present he studies for a PhD at the University of Westminster on strategic health care planning and forecasting long-term care demand in particular, and works part-time for the NHS London Procurement Programme. He has extensive experience in applying problem structuring and forecasting methods to develop health care modelling tools.