similar to: seasonal time serie with missing values

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hello, does anyone know about: a) simulating seasonal ARIMA models? arima out of package ts can fit it, but it does not look like it can simulates data from seasonal models b) fitting and simulating fractional seasonal ARIMA models? Hints will be appreciated, Henning -- Henning Rust Potsdam Institute for Climate Impact Research Dept. Integrated Systems Analysis Tel.: #49/331/288-2596

Hi all, I was wondering how to construct a seasonal differenced time series variable. I used the following code to construct a 12 span seasonal difference seasonal<-diff(V2, lag=12, differences=1) is this correct? thank you in advance joe [[alternative HTML version deleted]]

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

Hello everybody, I need to calculate seasonal means with temperature data for my work. I have 70 files coming from weather stations, which looks like this for example: startdate <- as.POSIXct("01/01/2006", format = "%d/%m/%Y") enddate <- as.POSIXct("05/01/2006", format = "%d/%m/%Y") date <- seq(from = startdate, to = enddate, by =

Hello, I am new to usin R - which is a great tool - and would like to know if R has a seasonal adjustment program for time series and/if it incorporates the Census Bureau's ARIMA x-12 seasonal adjustment program in any way? Thanks so much! Tony [[alternative HTML version deleted]]

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

Hi list, I would like to estimate and forecast the seasonal component of a series. My model which uses daily data would be something y t = alpha + beta x SeasComp t + gamma x OtherRegressors t. One approach to this would be use quarterly dummies, another to use a sine function. The first would cause a step change when we move from a season to another; the latter impose too much regularity in

Are any seasonal adjustment programs, like Tramo/Seats, Census X12 ARIMA or Berliner Verfahren implemented in R? I am doing a simulation study and I don't know how to adjust the series in R. The possibility to access external the exe.files of the seasonal adjustment programs seems to be quite difficult. Can anyone help me? Thanks, Ingo

Hi all, I just couldn't find a R function which can fit multiple seasonal patters... i.e. in the following code: *arima(x = data, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), ... *** * there can be only one "period", am I right? What if the data seem to have three different seasonality cycles, 5, 12, 21? Thanks a lot! * [[alternative HTML version

Dear R users, I would like to construct a matrix of seasonal dummy variables, such matrix can be written as follows(i.e format(T,4)) 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 . . . . . . . . etc I have written the following small program:

Hello, I have daily wind speed data and need to fit seasonal ARIMA model, problem is that my period is 365. But when I use arima(...) function, with period 365, I?m getting error message: ?Error in makeARIMA(trarma[[1]], trarma[[2]], Delta, kappa) : maximum supported lag is 350?. Can someone help me with this problem? Thank you Sincerely yours, Laura Saltyte

i am new to time series,whatever i know up till now,from that i have uploaded time series file & what to build arma model,but for that i want p & q values(orders) tell me how to calculate best p & q values to find best AIC values for model i am doing but giving error >bhavar<-read.table(file.choose()) #taking time series file > decompose(bhavar$V1) Error in

Hi All, a) Is it possible to estimate the strength of seasonality in timeseries data. Say I have monthly mean prices of an ten different assets. I decompose the data using stl() and obtain the seasonal parameter for each month. Is it possible to order the assets based on the strength of seasonality? b) which gives a better estimate on seasonality stl() or a robust linear model like

hallo to all I've to calculate an arima model and I need only the first and 365 th parameter and also the sar1 and the intercept, so I'm traing with: arima(X,order=c(365,0,0),seasonal=list(order=c(1,0,0),..),fixed=c(NA,rep(0,363),NA,NA,NA),transform.pars=F) but the error answer is: Error in polyroot(z) : polynomial degree too high (49 max) also there are problems in allocating memory

I have a zooreg object and I want to be able to generate a value for seasons and 11-day composites paste it onto my zoo data frame, along with year, month and days. Right now I have the following to work from: eg. dat.zoo.mdy <- with(month.day.year(time(dat.zoo)), cbind(dat.zoo, year, month, day, quarter = (month - 1) %/% 3 + 1, dow = as.numeric(format(time(dat.zoo), "%w")))) For

I have a seasonal time series. I want to calculate the annual mean value of the time series at its peak (say the mean of the three values before the peak, the peak, and the three values after the peak). The peak of the time series might change cycle slightly from year to year. # E.g., nPts <- 254 foo <- sin((2 * pi * 1/24) * 1:nPts) foo <- foo + rnorm(nPts, 0, 0.05) bar <- ts(foo,

Hi R-help, I'd like to extract the seasonal component of a short timeseries, and was hoping to use stats::decompose. I don't want to decompose the 'trend' component so I thought I should call decompose(x,filter=0). I think I've either misunderstood the filter argument or come upon a bug/feature in decompose. # EXAMPLE

Hi friends, I would like to construct a matrix of seasonal dummies with number of rows (observations)=100. such matrix is written as follows:[1 0 0 0;0 1 0 0;0 0 1 0;0 0 0 1;1 0 0 0;0 1 0 0;0 0 1 0;0 0 0 1;etc...] . I wrote the following program: T=100 br=matrix(0,T,4) { for (i in 1:T) for (j in 1:4) if i==j br[i,j]=1 if else (abs(i-j)%%4==0 br[i,j]=1 else br[i,j]=0 } z<-br z but