Course Overview

Many datasets naturally have a time series component: records collected over time, financial data, biological data signals such as brain waves or blood glucose levels, weather, and seasonal information. Practicing data scientists need to identify when they encounter time series data and when to apply suitable techniques. This course introduces the major topics in time series analysis and forecasting, including decomposition, exponential smoothing, regression, ARIMA models, and advanced machine learning approaches. Students will learn to analyze and forecast time series using R, evaluate model performance, and communicate results effectively. Real-world datasets will be used throughout, culminating in a complete end-to-end analysis project.

Prerequisites: ADS 501 and ADS 502

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