The course begins with the basics of forecasting and uncertainty and continues by covering all major available approaches, discussing their advantages as well as their limitations and explaining how they can be applied in practical situations. In addition, participants will be made aware of the value of combining more than one method in order to achieve the best results for their businesses organizations in terms of accuracy and uncertainty. The course also covers the newest Machine Learning (ML) methods to improve the accuracy of predictions and the better estimation of uncertainty. Furthermore, the main findings of the M Competitions are presented and their importance discussed. In addition, the R language is taught and its value to predict real-life series is demonstrated. Finally, major forecasting applications are described and fat-tailed uncertainty and its risk implications are introduced.

The course is designed for individuals working in finance, marketing, sales, retail, and operations, as well as consultants. Key applications covered in this course are from the above areas, using real-life data to illustrate the value added by both standard and state-of-the-art forecasting methods. Participants will acquire a practical understanding of how forecasting can help them to improve the accuracy of their predictions and estimate uncertainty correctly with an emphasis on what needs to be done in practice to reap the maximum benefits from the systematic usage of forecasting techniques.

In terms of prerequisites, a basic understanding of statistics is sufficient. No particular programming skill is conversely required, as all the necessary knowledge will be covered during the course.

All in all, the most important prerequisite is the willingness to learn how to become a good forecaster.

The course will last six weeks, and it will cover the following topics among others:

• Where to start and how to apply forecasting in your business
• How to estimate the future uncertainty in your predictions and take concrete actions to deal with such uncertainty, while considering its implications to risk
• Time series analysis, decomposition, and judgmental adjustments
• The use of statistical time series forecasting methods, like exponential smoothing and Theta
• Regression models and their planning value
• The basics of Machine Learning, like Neural Networks and Decision Trees, for time series forecasting
• Ways for improving forecasting accuracy through the combination of forecasts
• How to deal with extreme events and fat tails
• How to exploit the findings of the M Competitions to improve the forecasting function of your organization
• How to forecast and estimate uncertainty using the open free statistical language R. (Tutorials for those unfamiliar with R will be provided to help them in successfully completing the assignments of the course)
• Learn about key forecasting applications, like risk management, inventory management, sales and operations, budgeting, and long-term growth
• Apply what you have learnt to your own data, to improve your business decisions

Participants will be asked to work on two short assignments (one to be submitted at the end of the 4th week and the second at the end of the 6th week) and a final assignment (to be submitted two weeks after the end of the course), where participants will be asked to analyze a set of real data, which can come either from their own business environment (preferred, if available) or can be provided by the instructor instead.

All assignments will be reviewed by the teaching faculty. Only those who have successfully submitted the assignments will be eligible for the certificate of completion, which will be digitally verifiable.
The certificate will be issued in your name at no additional cost, upon successful completion of the course.
There is no extra cost for maintaining your certificate.

If, during the course, you decide not to work on the assignments, you can still access all course materials and participate in all activities, but no certificate will be issued.

Pasquale Cirillo is an associate professor at the MOFC of IFF at the University of Nicosia. His research interests include quantitative risk management, extreme value theory and combinatorial stochastic processes. He has been published in top international journals and is currently writing a book on fat tails. Besides his academic career, Pasquale has also collaborated with international institutions and many of the top private companies and banks as a statistical consultant. His MOOCs in risk management have been attended by more than a hundred thousand students from all over the world. He is a collaborator of Nassim Taleb and a proud amateur cook.

The University of Nicosia believes strongly in expanding accessibility to and affordability of higher education. Financial aid is available to students attending our professional training courses, through a combination of merit and need-based scholarships. To enquire, please contact [email protected], stating your country of residence and specific circumstances (academic achievements for merit-based scholarships or financial information for need-based ones).

Group registrations (2+ students from the same organisation) are also eligible for discounts. Please contact [email protected] for further information on our discount policy.

Payments can be made via credit card, bank transfer, Paypal or Bitcoin.

Through our online Moodle platform, you will have access to live sessions with the instructor and the opportunity to interact with fellow students. If you want, you can bring your own data and discuss your forecasting problems.

The course mainly focuses on interaction, real-life examples and case studies, thus there is only a limited amount of pre-recorded materials, mainly in Week 0.

Interaction with the instructors is always live, while the course team is available to answer any questions throughout the duration of the course

Participants will be asked to work on and submit a final assignment and analyze a set of deal data, which can come either from their own business environment (preferred, if available) or can be provided by the instructor instead.

All assignments will be reviewed by the Spyros Makridakis. Those who have successfully submitted the final assignment will be eligible for the certificate of completion, which will be digitally verifiable.

The certificate will be issued in your name at no additional cost, upon successful completion of the course. There is no extra cost for maintaining your certificate.