Time Series Analysis

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Determine how and when to apply different methods of time series analysis and how to test for goodness of fit using the software package X12
• Understand the theoretical bases of different methods of time series analysis including decomposition
• Decompose a time series into trend, seasonal and irregular components
• Understand and be able to apply the concepts and methods underlying the analysis of univariate time series, and the context for interpretation of results

• Difference of time series data compared to other data sets (equidistant observations, calendar effects, outliers)
• Basic concepts of time series: Stationarity, Ergodicity, Autocorrelations, Partial Autocorrelations
• Global models for trends and seasonals
• The periodogram and spectral analysis
• Local models and moving average methods
• ARIMA modelling and forecasting
• Exponential smoothing
• Estimation of unobservable components using a software package (X12ARIMA)

Teaching and learning methods

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Resources & Reading list

General Resources

Laboratory space and equipment required. Practical computing lab in X12ARIMA

Textbooks

Wei, W. S. (1994). Time Series Analysis: Univariate and Multivariate Methods. Addison-Wesley Publishing company.

Chatfield, C. (1996). The Analysis of Time Series: An Introduction. Chapman & Hall.

Harvey, A.C. (1993). Time Series Models.

Harvey, A.C. (1989). Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External