Friday 14:55
in Dynamicum
Forecasting can often feel like trying to make sense of unclear patterns—difficult to interpret but rich with potential. This talk clarifies the process, focusing on actionable steps for tuning forecasting models in professional environments where accuracy and performance drive business outcomes.
- Defining Clear Business Objectives:
Importance of aligning machine learning efforts with tangible business goals. Scoping forecasting problems and selecting appropriate success metrics.
- Data Preparation Techniques:
Cleaning data with a focus on business relevance and systematically enriching it In addition, we show how to tune the model by tuning the data nad the corresponding feature engineering.
- Feature Selection and Hyperparameter Tuning:
Advanced feature selection strategies and their impact on model performance. Techniques for identifying impactful features. Best practices for hyperparameter tuning and optimization strategies.
- The Role of interpretability and Generative AI in Model Tuning:
Automating feature generation. Hyperparameter optimization techniques using generative AI. model tuning through model interpretation
- Real-World Applications and Case Studies:
How Blue Yonder improved retail forecast accuracy. Lessons learned from industry case studies.
- Common Pitfalls and Best Practices:
Typical mistakes made during model tuning.
Best practices for ensuring model reliability and relevance. The importance of domain knowledge in successful forecasting.
Conclusion: Whether you are a seasoned data scientist or just starting your forecasting journey, this session will provide you with actionable insights to fine-tune your forecasting models effectively. Expect practical techniques, real-world examples, and expert tips that you can apply immediately. Join us and learn how better forecasts lead to better business decisions.
