Machine Learning 103: Advanced Time Series & Forecasting
Description
This advanced course explores powerful forecasting techniques, including SARIMAX, tree-based models, and deep learning for time series. Participants will gain hands-on experience in preprocessing, modeling, and evaluating complex temporal data using modern ML tools.
Indicative Duration: 8 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. SARIMAX |
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| 2. Tree-based models |
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| 3. Deep learning for time series |
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Learning Objectives
Upon completion of the course participants will be able to:
- Understand and apply the SARIMAX model for time series forecasting.
- Preprocess time series data for compatibility with tree-based models.
- Implement tree-based forecasting models on structured temporal data.
- Explore deep learning approaches for time series forecasting and understand their advantages.
- Evaluate and compare advanced forecasting techniques for different types of time series problems.
Target Audience
- Roles: Senior Data Scientists, ML Engineers, Forecasting Analysts, AI Researchers
- Seniority Levels: Advanced professionals with prior experience in time series modeling and machine learning
Prerequisite Knowledge
- Strong understanding of time series fundamentals
- Experience with Python and ML libraries (e.g., scikit-learn, pandas)
- Familiarity with basic forecasting models and evaluation metrics
- Completion of Machine Learning 102 or equivalent experience
Delivery Method
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
The training methodology combines presentations, live demonstrations, hands-on exercises and interactive discussions to ensure participants actively practice AI in realistic work scenarios.
