wq (version 0.4.8)

decompTs: Decompose a time series

Description

The function decomposes a time series into a long-term mean, annual, seasonal and "events" component. The decomposition can be multiplicative or additive, and based on median or mean centering.

Usage

decompTs(x, event = TRUE, type = c("mult", "add"), center = c("median", "mean"))

Arguments

x

a monthly time series vector

event

whether or not an "events" component should be determined

type

the type of decomposition, either multiplicative ("mult") or additive ("add")

center

the method of centering, either median or mean

Value

A monthly time series matrix with the following individual time series:

original

original time series

annual

annual mean series

seasonal

repeating seasonal component

events

optionally, the residual or "events" series

Details

The rationale for this simple approach to decomposing a time series, with examples of its application, is given by Cloern and Jassby (2010). It is motivated by the observation that many important events for estuaries (e.g., persistent dry periods, species invasions) start or stop suddenly. Smoothing to extract the annualized term, which can disguise the timing of these events and make analysis of them unnecessarily difficult, is not used.

A multiplicative decomposition will typically be useful for a biological community- or population-related variable (e.g., chlorophyll-a) that experiences exponential changes in time and is approximately lognormal, whereas an additive decomposition is more suitable for a normal variable. The default centering method is the median, especially appropriate for series that have large, infrequent events.

If event = TRUE, the seasonal component represents a recurring monthly pattern and the events component a residual series. Otherwise, the seasonal component becomes the residual series. The latter is appropriate when seasonal patterns change systematically over time. You can use plotSeason and seasonTrend to investigate the way seasonality changes.

References

Cloern, J.E. and Jassby, A.D. (2010) Patterns and scales of phytoplankton variability in estuarine-coastal ecosystems. Estuaries and Coasts 33, 230--241.

See Also

plotSeason, seasonTrend

Examples

# Apply the function to a single series (Station 27) and plot it:
y <- decompTs(sfbayChla[, 's27'])
y
plot(y, nc=1, main="")