Online Applied Forecasting:
Improving the Accuracy and Value of Your Predictions
Applied Forecasting Course
|Duration||Workload||Mode of Study||Tuition|
|6 weeks||10h/week||Online training||€2,000|
About the CourseThe M Applied Forecasting Course is a six week online course, (next intake 10 February 2020) covering all types of forecasting, both time series and regression, offering concrete insight to businesses on how to improve their accuracy realistically estimating the uncertainty in their forecasts and its implications to risk. In additional to the traditional forecasting methods it also present newer ones like Machine Deep and Cross Learning.
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 students the opportunity to harness 40 years of Professor Makridakis knowledge and experience and master forecasting, in six weeks. In addition, Nassim Nicholas Taleb will discuss fat tails and their implications to uncertainty and risk Dr. Vangelis Spiliotis will present the R popular forecasting software and how they can be used, while Gary Mulder will show the usage of the DL programs Auto ML and Gluon TS.
What the Course Covers
The course covers all forecasting methods and focuses on how they can be used practically to improve forecasting accuracy and the realistic estimation of uncertainty in future predictions
Who Should Take This Course?
The course is designed for executives in charge of finance, marketing, selling and operations as well as consultants. Key applications covered in this course are from the above areas using case studies to illustrate their usage. Participants will acquire a practical understanding of how forecasting can help them to improve the accuracy of their predictions, and what they need to do in practice to reap maximum benefits.
What Will You Learn?
The course will last six weeks and cover among other topics the following:
- Where to start and how to apply forecasting in your business
- Estimating the future uncertainty in your predictions and taking concrete actions to deal with such uncertainty
- Time Series forecasting and its use, utilizing available, free software programs
Using regression Models and its exploiting its planning value
Neural networks, deep learning and hybrid forecasting models and their usefulness
Improving forecasting accuracy through the combination of forecasts
Exploiting the findings of the M Competitions to improve the forecasting function of your organization
Using your own data and available/free software programs to forecast your own series
Finding out how to use such free software in your own firm
Who Will You Learn From?
Prof. Spyros Makridakis
and the other organizers of the M Competitions
Dr. Spyros Makridakis is a Professor at the University of Nicosia, where he is also a director of its Institute For the Future (IFF) and the 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 in 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 close to 5500 citations.
Professor 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 123,000 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. He is mentioned in Wikipedia in https://en.wikipedia.org/wiki/Spyros_Makridakis and https://en.wikipedia.org/wiki/Makridakis_Competitions
Session 1: Time Series Decomposition:
Seasonality, Trend-Cycle and Randomness
Session 2: Identifying Patterns/Relationships in the Data:
The Stat and the ML Approaches to forecasting.
Session 3: Graphical and Data Analysis Using the R and Other Software Packages
Session 4: Forecasting and Uncertainty
Session 5: The M Competitions:
The Use of Benchmarks, Simple vs. Sophisticated Methods, Combining Forecasts, Costs versus Accuracy
Session 6: Exponential Smoothing Models and the Theta Method
Session 7: Regression Methods
Session 8: ML, DL, CL and Hybrid Models (ML: Machine Learning, DL: Deep Learning, CL: Cross Learning)
Session 9: Statistical
Session 10: ML, DL and CL
Session 11: Forecasting for:
- Sales and Operations (S&OPS)
- Long-Term Growth and Strategy
Session 12: Fat-Tailed Uncertainty and related risk