Forecasting can often feel like interpreting vague signals—unclear yet full of potential. In this talk, we’ll cover advanced techniques for tuning forecasting models in professional settings, moving beyond the basics to explore methods that enhance both accuracy and interpretability.

You’ll learn:

How to set clear business goals for ML model tuning and align technical work with business needs, including balancing forecast granularity and accuracy and selecting statistically correct metric.

Practical data preparation methods, including business-driven data cleaning and detecting data problems with statistical and buiness driven approaches.

Advanced feature selection techniques such as recursive feature elimination and SHAP values, alongside hyperparameter tuning strategies including Bayesian optimization and ensemble methods.

How generative AI can support model tuning by automating feature generation, hyperparameter search, and enhancing model explainability through SHAP and LIME techniques.

Real-world case studies, including how Blue Yonder’s data science team optimized demand forecasting models for retail and supply chain applications.

We'll also discuss common mistakes like overfitting and data leakage, best practices for reliable validation, and the importance of domain knowledge in successful forecasting. Whether you're a seasoned data scientist or exploring time series forecasting, you'll gain advanced insights and techniques you can apply immediately.