 Decomposition of time series

The decomposition of time series is a statistical method that deconstructs a time series into notional components. There are two principal types of decomposition which are outlined below.
Contents
Decomposition based on rates of change
This is an important technique for all types of time series analysis, especially for seasonal adjustment.^{[1]} It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behaviour. For example, monthly or quarterly economic time series are usually decomposed into:
 the Trend Component T_{t} that reflects the long term progression of the series (secular variation)
 the Cyclical Component C_{t} that describes repeated but nonperiodic fluctuations, possibly caused by the economic cycle
 the Seasonal Component S_{t} reflecting seasonality (Seasonal variation)
 the Irregular Component I_{t} (or "noise") that describes random, irregular influences. Compared to the other components it represents the residuals of the time series.
An example of statistical software for this type of decomposition is the program BV4.1 that is based on the socalled Berlin procedure.
Kendall^{[2]} shows an example of a decomposition into smooth, seasonal and irregular factors for a set of data containing values of the monthly aircraft miles flown by UK airlines.
Decomposition based on predictability
The theory of time series analysis make use of the idea of decomposing a times series into deterministic and nondeterministic components (or predictable and unpredictable components).^{[1]} See Wold's theorem and Wold decomposition.
See also
 Hilbert–Huang transform
 Stochastic drift
References
 ^ ^{a} ^{b} Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0199206139
 ^ Kendall, Sir M.G. (1976) TimeSeries, Second Edition, Charles Griffin & Co.. ISBN 0852642415 (Fig. 5.1)
Categories: Time series analysis
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