FAQ
(please CC me as I have attempted to subscribe but got no reply)
arima(t, order = c(0, 0, 0), seasonal = list(order = c(1, 0, 2), period =
168))

Program received signal SIGSEGV, Segmentation fault.
0xb77e405a in getQ0 (sPhi=0xae17fc, sTheta=0xc8) at arima.c:775
775 rbar[ithisr++] = cbar * rbthis + sbar * xk;
(gdb)

What should I do about this? Do you want the data file?
Please let me know what I can do to help.

Many thanks in advance,

--
Jean-Luc

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  • Prof Brian Ripley at Oct 6, 2005 at 9:48 am
    A seasonal ARIMA model with period 168 is normally unrealistic: how long
    is the series? This model has several hundred parameters.

    I suggest you try arima0, as that is likely to use less memory, but either
    is going to be inefficient as you are essentially fitting 168 separate
    ARMA(1, 2) models for (I guess) each hour of the week.

    (The basic information we ask for in the posting guide such as the version
    of R and your platform is missing here.)
    On Thu, 6 Oct 2005 jfontain at free.fr wrote:

    (please CC me as I have attempted to subscribe but got no reply)
    arima(t, order = c(0, 0, 0), seasonal = list(order = c(1, 0, 2), period =
    168))

    Program received signal SIGSEGV, Segmentation fault.
    0xb77e405a in getQ0 (sPhi=0xae17fc, sTheta=0xc8) at arima.c:775
    775 rbar[ithisr++] = cbar * rbthis + sbar * xk;
    (gdb)

    What should I do about this? Do you want the data file?
    Please let me know what I can do to help.
    --
    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 272860 (secr)
    Oxford OX1 3TG, UK Fax: +44 1865 272595
  • Jfontain at Oct 6, 2005 at 10:27 am

    Quoting Prof Brian Ripley <ripley@stats.ox.ac.uk>:

    A seasonal ARIMA model with period 168 is normally unrealistic: how long
    is the series? This model has several hundred parameters.
    The series is 1000 long.
    I suggest you try arima0, as that is likely to use less memory, but either
    is going to be inefficient as you are essentially fitting 168 separate
    ARMA(1, 2) models for (I guess) each hour of the week.
    Correct guess! ARIMA gave me good results using the last 336 samples as input,
    so I though I'd try with the whole 1000. I am abviously not an expert
    statistician and I am working on a brute force approach by trying all the p, d,
    q and P, D, Q seasonal parameters...
    (The basic information we ask for in the posting guide such as the version
    of R and your platform is missing here.)
    Sorry:
    R : Copyright 2005, The R Foundation for Statistical Computing
    Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0
    on
    a Linux Fedora Core 3 machine with 2 Xeon processors:
    Linux version 2.6.10 (gcc version 3.4.2 20041017 (Red Hat 3.4.2-6.fc3)) #1 SMP

    Thanks for your help!



    --
    Jean-Luc

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