[R] stationary "terminology" time series question
Prof Brian Ripley
ripley at stats.ox.ac.uk
Fri Jun 27 08:49:10 CEST 2008
Stationarity is a statement about a stochastic process, not about a single
realization. It is a statement about what might have happened, not what
did happen.
A sine wave with a random (uniform) wave is stationary, and indeed the
superposition of such waves is the spectral decomposition. A sine wave
with a fixed phase is not.
So stationarity is a modelling assumption. This comes up often in the
geosciences when you have just one realization. For example look at earth
temperature series -- whether they are stationary is a modelling
assumption, and may in part depend on the timescale involved. But for
example James Lovelock's Gaia hypothesis implies stationarity.
On Thu, 26 Jun 2008, markleeds at verizon.net wrote:
> This is not exactly an R question but the R code below may make my question
> more understandable.
>
> If one plots sin(x) where x runs from -pi to pi , then the curve hovers
> around zero obviously. so , in a"stationary in the mean" sense,
> the series is stationary. But, clearly if one plots the acf, the
> autocorrelations at lower lags are quite high and, in the "box jenkins"
> sense, this series is clearly not stationary in terms of its acf. so, i'm
> confused in terms of what ithe statistical definition of stationary is
> as box jenkins define it ?
You are crediting Box and Jenkins (sic) with something that was long
established before them. Using the ACF needs only second-order
stationarity, without which it is not defined.
> I don't have their text in front of me but I don't remember them having an
> example such as below when they talk about needing to difference series
> to achieve stationarity. thanks for any insights or a text that talks about
> this.
Almost all good texts do. Perhaps Box and Jenkins have confused you by
majoring on ARIMA models, which can be made stationary by differencing --
not a general attribute but useful for the sales forecasting series that
(I am told) was their primary motivation.
> x <- seq(pi,-pi,by=-pi/4)
> y <- sin(x)
> plot(x,y)
> acf(y)
>
> P.S: this question arose because a colleague asked me to look at the plot of
> his series and the associated acf and he claims it's a stationary series and
> I'm trying to explain to him that it is not and to try to use the acf to
> build a model for it is not reasonable.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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