Time-Dependent Statistical Inference provides a comprehensive exploration of statistical methods tailored for analyzing data that varies over time. Participants will engage with advanced techniques that enable them to model, interpret, and predict time-dependent phenomena effectively. The course emphasizes practical applications through project-based learning, ensuring that participants not only grasp theoretical concepts but also apply them in real-world scenarios.
Throughout the program, learners will delve into the intricacies of time series data, exploring methodologies such as ARIMA modeling, seasonal decomposition, and forecasting techniques. By the end of the course, participants will have the opportunity to publish their findings in Cademix Magazine, showcasing their expertise and contributing to the academic community. This program is structured to enhance analytical skills, making participants valuable assets in various professional settings.
Introduction to Time Series Data and Characteristics
Stationarity and Non-Stationarity Concepts
Autocorrelation and Partial Autocorrelation Functions
ARIMA Modeling: Theory and Application
Seasonal Decomposition of Time Series
Exponential Smoothing Techniques
Forecasting Accuracy Metrics
Advanced Time Series Regression Models
Intervention Analysis and Time Series Experiments
Final Project: Develop a Comprehensive Time Series Analysis on a Real Dataset