similar to: seasonal fractional ARIMA models

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, 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,

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

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

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

> 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

I'd like to play with ARIMA models of stock prices, but I am a complete novice. Could some kind soul explain the relationship among packages "ts", "tseries", "dse", "dse2", and "fracdiff"? Are they 'competing' products or does one depend on another? Where would be the best place for a novice to begin? Thanks for any advice. PS. I

Dear folk, I am stempting to estimate a vector error correction model using a seemingly fractionally integrated multivariate time series. The *fracdiff *package provides tools to estimate degree of fractional integration. But *fracdiff *can't help me to: 1. test equality of two degrees of fractional integration, say d1=d2? 2. estimate a multivariate cointegrating error correction model,

Thanks for everyone's help with zoo -- I think I've got my data set ready. (The data consists of surface weather temperatures, from 2002 to 2005, one observation per hour. Some values are missing... i.e. NA) I have three goals: GOAL #1:Get the data in proper time series form, preserving frequency information: > w4.ts <- as.ts( w3.zoo, frequency=(1/3600) ) I hope that 1/3600

Hi, I have the following code from Splus that I'd like to migrate to R. So far, the only problem is the arima.filt function. This function allows me to filter an existing time-series through a previously estimated arima model, and obtain the residuals for further use. Here's the Splus code: # x is the estimation time series, new.infl is a timeseries that contains new information # a.mle

Hello! I installed the fSeries package to get some farima time-series which i tried with farimaSim, but unfortunately i got always an error. I tried it this way: > farimaSim(n = 1000, model = list(ar = 0.5, d = 0.3, ma = 0.1), method="freq") Error in farimaSim(n = 1000, model = list(ar = 0.5, d = 0.3, ma = 0.1), : ... used in an incorrect context Some ideas? Regards, ___

I am puzzled by the warning message in the output below. It appears whether or not I fit the seasonal term (but the precise point of doing this was to fit what is effectively a second seasonal term). Is there some deep reason why AR parameters ("Warning message: some AR parameters were fixed: ...") should somehow intrude into the fitting of a model that has only MA terms? >

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

Hello everyone, I was trying to fit an arima model to a daily historical data, but, for some reason, havent been able to. I basically have 212 observations (from 12/1/2012 to 06/30/2013) containing the number of transits for a particular vessel. The following messages are produced by R: dailytrans.fit<-arima(dailytrans$transits, order=c(0,1,2), seasonal=list(order=c(0,1,2), period=365),

Hello, I have a several time series, which I would like to check for their best fitted Arima model (I am checking for the lowest aic value). Which lets me raise two questions: 1) is there are more efficient way, than using 6 for-loops? 2) sometimes the system cannot calculate with given parameters - is there a more efficient solution than I found? I hope, you can help me to make this

Hi people, I´m trying to development a simple routine to run many Arima models result from some parâmeters combination. My data test have one year and daily level. A part of routine is: for ( d in 0:1 ) { for ( p in 0:3 ) { for ( q in 0:3 ) { for ( sd in 0:1 ) { for ( sp in 0:3 ) { for ( sq in 0:3 ) {

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

Colleagues I am a new to R but already love it. I have the following problem: I fitted arima model to my time series like this (please ignore modeling parameters as they are not important now): x = scan("C:/data.txt") x = ts(x, start=1, frequency=1) x.fit<-arima(x, order = c(1,0,0), seasonal = list(order=c(0,0,1))) Now I want to use this model for forecasting and backtesting (!).