similar to: Bug in arima?

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

While adding support for branch instructions in my backend, I run into a trouble. The code to handle branches looks like: void visitBranchInst(BranchInst& BI) { BB->addSuccessor (MBBMap[BI.getSuccessor(0)]); if (BI.isConditional()) BB->addSuccessor (MBBMap[BI.getSuccessor(1)]); ........... BuildMI(BB,

Folks, I'm running into something which looks like a bug in linearscan allocator. Of course I can't be 100% sure it's not some unobvious mistake on my part, so I'd like to hear your opinion. First, I attach two files -- LLVM asm and the asm for my target. The problem with assembler is: on line 171 it uses register gr2, which is copied from gr6 above, on line 161. The only

Hello, I'm running a number of arima models using the "arima" function. Often, when lag length gets too high, these model don't converge and an error message appears as this: > reg <- arima(y,order=c(7,0,7),xreg=isr) Warning message: In arima(y, order = c(7, 0, 7), xreg = isr) : possible convergence problem: optim gave code=1 In this case, when you print the results

Dear list and Martin, I'm testing different approaches to fit an electricity demand time series and come upon the fracdiff package (v 1.3-1) for fitting fractional ARIMA models. The following questions are motivated by this package. 1. Despite having a help page, the residuals and fitted functions don't seem to have implementation, or did i miss something obvious? Alternatively, having a

Dear all, The standard call to ARIMA in the base package such as arima(y,c(5,0,0),include.mean=FALSE) gives a full 5th order lag polynomial model with for example coeffs Coefficients: ar1 ar2 ar3 ar4 ar5 0.4715 0.067 -0.1772 0.0256 -0.2550 s.e. 0.1421 0.158 0.1569 0.1602 0.1469 Is it possible (I doubt it but am

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is intended for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

This patch set is for the next merge window. They are just enhancements of the already merged patches or ia64 porting from x86 paravirt techniques and that their quality is enough for merge. This patch set is for binary patch optimization for paravirt_ops which depends on the patch series I sent out, ia64/pv_ops, xen: more paravirtualization. The binary patch optimization is important on native

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

This patch set is for binary patch optimization for paravirt_ops. The binary patch optimization is important on native case because the paravirt_ops overhead can be reduced by converting indirect call into in-place execution or direct call. The first patch imports helper functions which themselves doesn't interesting things. The second patch replaces the indirect function calls with a

Dear all, I need to compute the ACF (autocorrel) of an AR6 process, given the values of its parameters (w1,w2,w3,w4,w5,w6). First, I notice that there is an error as soon as the sum of the wi equals 1 : "Error in drop(.Call("La_dgesv", a, as.matrix(b), tol, PACKAGE = "base")) : system is computationally singular: reciprocal condition number = 1.00757e-18"

I had posted about winbind picking the wrong domain to this list about a week ago and this was the problem I was still trying to solve when I must have messed something else up. SO! Winbind WAS working (even though it was picking the wrong domain) I re-ran smbpasswd -j LMC -r <our PDC> -U administrator I get prompted for the password, enter it and I am told that I joined the LMC

On Tue, 8 Jun 2004, Vladimir Prus wrote: > While adding support for branch instructions in my backend, I run into a > trouble. The code to handle branches looks like: > The machine code after instruction selection is: > > entry (0x8681458): > %reg1024 = load <fi#-1> > %reg1025 = load <fi#-2> > setcc %reg1024, %reg1025 >

Estimados quería realizarles una consulta: Trabajo con una tabla con 23 registros, la cual tiene en la columna "*ar4*" valores de una variable llamada ranking para cada registro, y una columna "percentil" donde le voy a colocar el nivel de percentil en el que se encuentra ese valor (ar4) de cada uno de esos 23 registros. La tabla es la siguiente:: código padre n ar4