About

Course overview

Welcome to Advanced Topics in Statistics: Applied Time Series! This course focuses on analyzing and forecasting time-dependent data through modern statistical methods. Using real-world case studies and hands-on projects, you will learn to build, evaluate, and compare models like ARIMA and ETS (Error-Trend-Seasonality) to solve problems in economics, finance, public health, and beyond.

We will emphasize practical skills, including exploratory time series analysis, model diagnostics, and state-space frameworks, using R for computation and visualization. By the end of the course, you will be able to interpret complex time series patterns, generate reliable forecasts, and communicate insights effectively to diverse audiences.

Learning Objectives

• Learn basic analysis of time series data and exploratory data analysis.
• Understand and apply time series regression models.
• Master autoregressive (AR) and moving average (MA) models.
• Master ARIMA, ETS, and state-space models for forecasting.
• Compare and select models (e.g., ETS vs ARIMA) using diagnostic tools.
• Utilize R for computation, visualization, and analysis of time series data.
• Develop skills to interpret and communicate time series results effectively.