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Unit of study_
ODAT5022: Applied Time Series Analysis
2025 unit information
Predict the future by mastering the art of time series analysis. Understanding and modelling time series data is important in a wide range of domains, for example, energy demand, retail sales, healthcare, web traffic, weather, finance, and economics. This unit will equip you with the knowledge and tools to confidently tackle the complexities of time series data and apply modern forecasting techniques using real-world data. Each week brings a new set of challenges, guiding you through methods like exponential smoothing, ARIMA, and dynamic regression models. You will learn how to effectively visualise and communicate data collected over time, fit a variety of models and assess their performance, choose between completing models, and quantify the uncertainty around your forecasts. Through practical assignments and projects, you will hone your ability to learn from the past and make reliable, evidence-based predictions about the future.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Postgraduate |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analysis Foundations. It would be an advantage to also take ODAT5021 to further build your statistical and computational skills before attempting ODAT5022. |
At the completion of this unit, you should be able to:
- LO1. explain and compare different forecasting methods in terms of their assumptions and applicability across various types of time series data
- LO2. discuss the challenges of implementing forecasting techniques in practice
- LO3. apply appropriate statistical techniques to conduct time series forecasting, utilising software tools to visualise, model, predict, and interpret given data
- LO4. evaluate and communicate the accuracy and effectiveness of forecast models
- LO5. design and implement a time series forecasting project using real-world data
Unit availability
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