Applied Time Series Analysis and Forecasting
Gain a practice-oriented foundation in forecasting and time series analysis. Balancing statistical principles with hands-on work in R, students analyze temporal data, design workflows, and evaluate predictive performance. Topics include the forecasting lifecycle, exploratory analysis, and structural change. Explore time series through stationarity, autocorrelation, autoregressive moving average models, and unit root testing. Broaden skills with time series regression, exponential smoothing, Prophet, and vector autoregressions. Unify approaches under the state space framework using the Kalman filter to build, evaluate, and monitor deployed models for real-world decisions.
Topics
- Recall and define key concepts in time series and forecasting, including data structures, the components of a series (trend, seasonality, and cycles), error metrics, and the major model families, while recognizing core challenges such as nonstationarity, structural change, and time-varying dynamics. Explain the theoretical foundations and assumptions of the major time-series models, and describe how different models are selected or adapted to address the data and the decision context. Prepare and transform datasets through data wrangling and exploratory data analysis (EDA), and apply time-series models across diverse contexts. Investigate time series behavior and model output by diagnosing stationarity, interpreting autocorrelation structure, identifying structural breaks and outliers, decomposing a series into its components, and checking whether a model is well specified. Critically assess and compare time-series models by examining their accuracy, robustness, assumptions, and interpretability, and justify methodological choices with evidence grounded in both statistical reasoning and domain context. Design an end-to-end forecasting system that runs on real data, monitors its performance over time, and communicates results to technical and non-technical audiences.
Official catalogue course objectives and student learning outcomes
Prerequisites & course information
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Sources
- Course catalogue ↗2026-2027