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Time Series Overview
Investigating and modelling time series data, including:
• qualitative features of time series plots; recognition of features such as trend (long-term direction), seasonality (systematic, calendar related movements) and irregular fluctuations (unsystematic, short-term fluctuations); possible outliers and their sources, including one-off real world events, and signs of structural change such as a discontinuity in the time series
• numerical smoothing of time series data using moving means with consideration of the number of terms required (using centring when appropriate) to help identify trends in time series plot with large fluctuations
• graphical smoothing of time series plots using moving medians (involving an odd number of points only) to help identify long-term trends in time series with large fluctuations
• seasonal adjustment including the use and interpretation of seasonal indices and their calculation using seasonal and yearly means
• modelling trend by fitting a least squares line to a time series with time as the explanatory variable (data de-seasonalised where necessary), and the use of the model to make forecasts (with re-seasonalisation where necessary) including consideration of the possible limitations of fitting a linear model and the limitations of extending into the future.
Assessment Tasks Overview
- Chapter Tests
- Combined SAC (At the end of Chapter 6)
Learning Goals/Success Criteria
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