Statistics
ST416: Time Series Analysis (5 ECTS)
MODULE AVAILABILITY SUBJECT TO SUFFICIENT ENROLLMENT NUMBERS
This module mixes statistical theory and practice to develop and implement models for the analysis of time series data. The module is a relatively advanced course that requires knowledge of probability and mathematical and applied statistics such as that in the modules ST235, ST236 and ST313.
Taught in Semester(s) I. Examined in Semester(s) I.
Workload: 34 hours (24 Lecture hours, 10 Tutorial hours).
Module Learning Outcomes.
On successful completion of this module the learner should be able to:
- Understand and appreciate the role of time series analysis in modelling data.
- Describe time series data, including partitioning into trend, seasonal/cyclical and random components.
- Fit models to estimate and sometimes control the expected response, make inferences about parameters, and perform rigorously-justified forecasts.
- Perform time series in the time domain, using ARIMA and other models.
- Perform time series analysis in the frequency domain, including signal extraction.
- Apply time series analysis in various areas such as engineering, climatology, applied physics, environmental science, marine science, applied finance, economics, etc. Use statistical software like R, SPSS and MInitab to analyse time series data.
Indicative Content
This module provides a fairly rigorous approach to the analysis of time series analysis. Topics covered include:
- Descriptive approaches.
- Autocorrelation and partial autocorrelation functions.
- Stationary and nonstationary processes.
- Departures from ordinary multiple regression models to accommodate dependence.
- ARIMA and Seasonal ARIMA: model identification, estimation, hypothesis testing, diagnostics, and forecasting.
- Spectral Analysis: Periodicity, spectral representation theorem, periodograms, signal extraction, optimal filtering.
Module Resources
- Shumway, Robert H. and Stoffer David S., Time Series Analysis and Its Applications: With R Examples; 3rd Ed.. Springer Texts in Statistics, 2010.
- Cryer, Jonathan D. and Chan Kung-Sik, Time Series Analysis: With Applications in R., 2nd Edition. Springer Texts in Statistics, 2008.
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