Courses

This 5-day course covers time series analysis methods and their applications in energy operations. Participants will learn to explore, analyze, and forecast temporal data using classical statistical techniques and modern deep learning approaches. The course addresses key challenges in energy data including anomaly detection, pattern recognition, and predictive modeling. Through hands-on work with real energy industry datasets—from oil production and well logs to sensor readings and equipment monitoring—participants will develop practical skills for solving time series problems in their operations.

Course Objectives

• Master time series fundamentals: Learn exploratory data analysis, visualization techniques, decomposition methods, and frequency/time domain analysis for understanding temporal patterns in energy data.
• Apply diagnostic techniques: Identify outliers, detect anomalies, recognize change points, and classify time series patterns using appropriate statistical and machine learning methods.
• Build forecasting models: Develop univariate, multivariate, and probabilistic forecasting approaches to predict production, demand, equipment behavior, and operational parameters.
• Leverage modern architectures: Understand and apply recurrent neural networks, transformer models, and generative AI approaches to complex time series tasks in energy applications.
• Implement practical solutions: Use similarity measures, explainability techniques, and responsible AI practices to deploy time series models that support operational decisions.

Learning Outcomes

Upon completion of this course, participants will be able to:

• Perform exploratory analysis and visualization of time series data, identifying trends, seasonality, and underlying patterns in energy operations data.
• Apply decomposition techniques and frequency domain methods to understand temporal structure, remove noise, and handle missing values in sensor and production data.
• Detect anomalies, identify change points, and classify time series patterns in equipment monitoring, well logs, and operational datasets.
• Build and evaluate forecasting models for production prediction, demand estimation, and equipment behavior using both classical and deep learning methods.
• Implement recurrent neural networks and transformer architectures for complex temporal modeling tasks in energy applications.
• Apply zero-shot learning approaches and generative AI techniques to solve time series problems with limited training data.
• Use dynamic time warping and similarity measures to compare temporal patterns across wells, facilities, or operational scenarios.
• Explain model predictions using interpretability techniques and apply responsible AI practices in operational time series applications.