Hi Mark
Thank you for the response. The problem is biologically I can’t really avoid
using the irregular time step (daily samples taken at irregular intervals)
I guess generally I’m not really doing time series analysis (my model is a
multiple regression), I just wanted to use time series techniques to test for
stationarity.
Therefore would it be feasible to aggregate the data to say a weekly time step
which is regularly spaced, use one of the methods to check were the data is
stationary, then use the irregular daily time step data in the multiple
regression model?
Thanks again
Tom
From: Mark Leeds [mailto:marklee...@gmail.com]
Sent: Thursday, July 03, 2014 12:09 AM
To: Worthington, Thomas A
Subject: Re: [R] Checking whether a time series is stationary with irregular
spaced data
hi thomas: I don't deal with irregularly spaced data but the standard time
series techniques
don't apply there. there may be other ways but one workaround is to interpolate
so
that it's regularly spaced and then use that regularly spaced series.
On Wed, Jul 2, 2014 at 6:26 PM, Worthington, Thomas A
<thomas.worthing...@okstate.edu> wrote:
I attempting to model the relationship between water temperature and air
temperature. The seasonal component of the temperature time series has been
modeled using a sinusoidal function, leaving the air and water temperature
residuals. I want to model the relationship with
M5<- gls(Water ~ Air +Air1 +Air2, correlation = corCAR1(form =~ Date))
Where Water is the water temperature residual, Air is the air temperature
residuals at 1 and 2 day lags. I have included an autocorrelation structure
that takes into account the fact that the water temperature were taken at
irregular spaced intervals.
I would like to test whether the time series is stationary, I found a blog post
that used the following graphical methods and tests (Cent_Water is the water
temperature centered by subtracting the mean value)
Acf(Cent_Water)
Pacf(Cent_Water)
Box.test(Cent_Water, lag=20, type="Ljung-Box")
adf.test(Cent_Water, alternative="stationary")
kpss.test(Cent_Water)
Are these methods useable with irregular spaced data as I believe it is not
possible to use Acf?
Any suggestions would be greatly appreciated
Best wishes
Tom
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