Applied Forecasting

Professional Certificate Course

Master forecasting and harness 40 years of knowledge and experience in just 6-weeks

Online professional forecasting course – earn a certificate that is digitally verifiable on the Bitcoin Blockchain

Learn how to improve accuracy, correctly estimate forecasting uncertainties and figure out its implications to risk

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About the Course:

The Applied Forecasting Course is a six-week online course, (next intake 24 May 2021) covering all types of forecasting methods including time series and regression. The course offers individuals and businesses concrete insights on how to improve their accuracy realistically and estimate the uncertainty in their forecasts, while considering its implications to risk. In addition to the traditional statistical forecasting methods, Machine Learning ones like Neural Networks and Decision Trees, will also be covered.

The most important advantage of the course is its emphasis on hands-on learning by encouraging participants to use actual data to both predict and estimate the uncertainty of their forecasts and its implication to risk. This course will offer participants the opportunity to harness 40 years of Professor Makridakis’ knowledge and experience, and master forecasting in six weeks. In addition, Dr. Cirillo will cover extreme events and fat-tails, and their implications to forecasting and uncertainty, while Dr. Spiliotis will present both the traditional statistical forecasting methods, and the more advanced, Machine Learning ones.

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.

Who you will learn from:

The course is led by renowned professor Spyros Makridakis, Director, Institute For the Future (IFF), University of Nicosia and Evangelos Spiliotis, National Technical University of Athens. There will also be experts teaching, specializing in topics such as Deep Learning (DL) and Machine Learning (ML) as well as forecasting for intermittent inventory demand.
Spyros Makridakis is founder of the Makridakis Open Forecasting Center (MOFC). He is also an Emeritus Professor at INSEAD and the University of Piraeus. He has authored, or co-authored, twenty-four books and more than 270 articles. His book Forecasting Methods for Management, 5th ed. (Wiley) has been translated into twelve languages and sold more than 120,000 copies while his book Forecasting: Methods and Applications, 3rd ed. (Wiley) has been a widely used textbook in the forecasting field with more than 5,300 citations.
Spyros Makridakis was the founding editor-in-chief of the Journal of Forecasting and the International Journal of Forecasting and is the organizer of the M (Makridakis) Competitions. His article “Statistical and Machine Learning Forecasting Methods: Concerns and ways forwards” has been viewed/downloaded more than 155,000 times in PLOS ONE where it was published in March 2018 while his paper “The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms” (Futures, March 2017) is the most downloaded one of the journal. In November 2019, his citations in Google Scholar had surpassed 19,300.

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.

Evangelos Spiliotis is a Research Fellow at the Forecasting & Strategy Unit, National Technical University of Athens (NTUA), where he also serves as Coordinator. He graduated from School of Electrical and Computer Engineering, NTUA in 2013 and got his PhD in 2017. His research interests are time series forecasting, decision support systems, machine learning, optimisation, energy forecasting, energy efficiency and conservation. He has conducted research and development on tools for management support in many National and European projects. He co-organized the M4 Forecasting Competition.

Course Schedule

Getting started with R

To be provided as a recorded lecture before the beginning of the course for anyone lacking a programming background in R. Students will be able to ask questions during the course.

Taught by Pasquale Cirillo

During Week 1 there will be a preparatory session, Session 0, during which, a tutorial will be offered titled Introduction to R. This contains some videos and reading materials meant to help students familiarise themselves, with R, the programming language of the course. In Session 0, you will also find some hints and tips to get the most out of the Applied Forecasting course. During Session 0 there will be no live classes. You will be provided with a recorded lectureA pre-knowledge of R is not necessary.

Students will be able to ask questions during the live sessions.

All course material will be available online one week before the official starting date of the course.

Live classes will start in Week 1 Session1.

Week 1: Introduction to Forecasting

Session 0: Recorded Tutorial: Getting started with R

Taught by Pasquale Cirillo

Session 1: Time Series Decomposition

Seasonality, trend, cycle and randomness, data relationships

Taught by Spyros Makridakis

Session 2:Forecasting and Uncertainty

Understanding, measuring and dealing with various types of uncertainty

Taught by Spyros Makridakis

Week 2: Statistical Forecasting

Session 3: The M Competitions

Benchmarks, simple vs. sophisticated methods, combining forecasts, computational costs versus accuracy, the end of forecasting winter, simple ML methods

Taught by Spyros Makridakis

Session 4: Statistical Forecasting Methods

Naïve methods, exponential smoothing models, and the Theta method

Taught by Evangelos Spiliotis

First bi-weekly assignment (to be submitted at the end of week 4)

Week 3: Explanatory and Machine Learning Methods

Session 5: Linear Regression

Using explanatory variables to predict the future

Taught by Spyros Makridakis

Session 6: Machine Learning, Deep Learning, Cross Learning, and Hybrid Models

An introduction to Machine Learning, its variants, and its state-of-the-art implementations

Taught by Evangelos Spiliotis

Week 4: Advanced Machine Learning Methods with Applications

Session 7: Advanced Machine Learning Methods

Neural Networks and Regression Trees

Taught by Evangelos Spiliotis

Session 8: Case study

Application of Machine Learning methods in energy prices forecasting

Taught by Evangelos Spiliotis

Second bi-weekly assignment (to be submitted at the end of week 6)

Week 5: Tail risk and uncertainty

Session 9: Extremes and Fat tails

Taught by Pasquale Cirillo

Session 10: Tail risk and modeling

Taught by Pasquale Cirillo

Week 6: Forecasting at work

Session 11: Some successful forecasting applications 

Taught by Pasquale Cirillo

Session 12: The limits of forecasting

Taught by Pasquale Cirillo

Final assignment (to be submitted two weeks after the end of the course

Still have questions?

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.

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