Join us to start creating a mindset of business leaders responsible for the future together
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This course is for:
By the end of the course, participants will be able to effectively select and apply forecasting models using specialized software.
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There is often confusion between forecasting methodologies and predictive modeling using supervised machine learning algorithms. While the latter relies on external information for its predictions, forecasting uses its own data.
This workshop aims to provide a comprehensive understanding of all forecasting methods and how to apply them for near-future predictions. It will cover basic models and then explore the evolution of various methods, enabling participants to use them effectively. Understanding all quality indicators will help participants select the best forecasting model for their businesses.
Day 1:
- Linear and Polynomial trends
- Exponential, Power, and Logarithm trends.
Day 2:
- Averaging and Moving Averages.
Day 3: Exponential Smoothing
- Simple, Double, and Triple models in exponential smoothing.
Day 4:
- Time Series
- ARIMA and Box Jenkins method
- Comprehensive colored PPT documents.
- Supervised ML vs. Forecasting approach.
- Stationary, Additive, and Multiplicative models.
- Proprietary tools solutions.
- Quality measures of forecasting models.
- "White Noise” data.
- All-in-one solution method.
- Selecting the Fit model.
- Forecasting or Linear Regression?
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We have partnerships with international
professional organizations that specialize in professional training and have unique and up-to-date quality programs for our students.
Join us to start creating a mindset of business leaders responsible for the future together
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