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Time series data is essential in fields like finance, energy, healthcare, and climate science. This course introduces tools and techniques for analyzing, forecasting, and detecting anomalies in time series data. Participants will gain skills to uncover patterns, make accurate predictions, and identify unusual behavior, which is crucial in many applications. We cover both traditional methods and advanced machine learning approaches, providing a comprehensive toolkit to tackle diverse challenges.
Besides discussing time series methods, we will also explore how to adapt non-time series models for sequential data through feature engineering. Furthermore, we will discuss how identifying anomalies in time series data can highlight system failures, outliers, or unexpected changes, adding valuable insight to the forecasting process as well.
Participants will learn model selection, training strategies, and performance evaluation, with a focus on handling uncertainty and understanding model assumptions. Best practices for avoiding pitfalls in time series analysis will also be covered. Anomaly detection will be integrated into the analysis pipeline, ensuring the development of robust models that handle unusual patterns effectively.
This course is ideal for those looking to enhance their predictive modeling and anomaly detection skills. Through hands-on exercises and real-world examples, participants will develop the skills to ensure accurate, interpretable, and actionable results.
Learning objectives
- Understand Time Series Fundamentals
- Understand Anomaly Detection Problem
- Train and Evaluate Machine Learning Models
- Handle Uncertainty Using Probabilistic Methods
- Preprocess Data and Do Feature Engineering
- Work with Real Data and Avoid Common Pitfalls
