similar to: Forecasting by using ARFIMA(0, d, 0) models in R

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

please help.. I have estimate the value of parameter for AR,MA and fractional d.but I have problem on having the right command for forecasting ARFIMA model.please help...... -- View this message in context: http://r.789695.n4.nabble.com/how-to-make-ARFIMA-forecast-by-using-r-tp3869928p3869928.html Sent from the R help mailing list archive at Nabble.com.

I would like to compute the next 15 observations for an ARFIMA(2,1,0) model along with confidence intervals. Can someone provide code? Many thanks. Jill ____________________________________________________________________________________ [[elided Yahoo spam]]

>>>>> akshay kulkarni >>>>> on Wed, 31 May 2023 20:55:33 +0000 writes: > dear members, > I am using arfima() from forecast package to model a time > series. The following is the code: >> LYGH[[202]] > [1] 45.40 3.25 6.50 2.15 >> arfima(LYGH[[202]]) > Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma,

Dear Martin, Sad that the bug is beyond your ken... Fortunately, the error happens only rarely...The length of LYGH was 719 and there were only two such errors..I will just replace them with NA and make do. By the by, what if I send LYGH as an attachment to your actual mail ( not the r-help mail)? Will it help? Can you then pinpoint the cause? Or should I raise a bug

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

dear members, I am using arfima() from forecast package to model a time series. The following is the code: > LYGH[[202]] [1] 45.40 3.25 6.50 2.15 > arfima(LYGH[[202]]) Error in .fdcov(x, fdf$d, h, nar = nar, nma = nma, hess = hess, fdf.work = fdf$w) : NA/NaN/Inf in foreign function call (arg 5) I tried viewing .fdcov() with the following code:

Hello! I used the function fracdiff(dn, nar=1, nma=1) and got the values of d, ar and ma coefficients. Also another coefficients were get under fdGPH, fdSperio. How could I get the forecasts in these models? Thank you very much [[alternative HTML version deleted]]

Hello everybody! I have a problem when I try to perform a forecast of an ARIMA model produced by an auto.arima function. Here is what I'm doing: c<-auto.arima(fil[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE,trace=TRUE) # fil[[1]] is time series of monthly data ARIMA(0,0,0)(0,1,0)[12] with drift : 1725.272 ARIMA(0,0,0)(0,1,0)[12] with drift

Dear R users, I'm trying to use the "strucchange" package to determine structural breaks in an ARFIMA model. Unfortunately I'm not so familiar with this topic (and worse, I'm a beginner in R), so I don't know exactly how to specify my model so that the "Fstats","sctest" and "breakpoint" functions to recognize it and to calculate the

Please I want to perform full data analysis using ARFIMA model but I dont know the right package that can perform all the necessary test on the time series data. ERIC AIDOO [[alternative HTML version deleted]]

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

Hello all, This is a request for help from somebody who has the Ox interfaces working in R. I am trying to get the Ox interfaces working for Arfima and Garch modelling. However, I am having several problems: 1. The link to download G at rch_v40 does not work. Does anybody have a copy to email to me please? 2. Various guides offer different instructions for installing Ox in the correct place

Hola, ¿hay alguna librería que sirva para modelizar la inflación utilizando un modelo modelo fraccionalmente integrados ARFIMA-STVGARCH? Saludos, Sebastián.

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Dear all: I'm a newbie and an amateur seeking help with forecasting the next in a non-stationary time series, with constraints of 1 (low) and 27 (high) applicable to all. What I need help with is the solution concept. The series has 439 observations as of last week. I'd like to analyze obs 1 - 30 (which are historical and therefore invariate), to solve for 31. The history: Obs 1

Hi Frances, I have not touched the system.fit package for quite some time, but to solve your problem the following two pointers might be helpful: 1) Recast your model in the revised form, i.e., include your identity directly into your reaction functions, if possible. 2) For solving your model, you can employ the Gau?-Seidel method (see https://en.wikipedia.org/wiki/Gauss%E2%80%93Seidel_method).

Who was speaking about non-linear models in the first place??? The Klein-Model(s) and pretty much all simultaneous equation models encountered in macro-econometrics are linear and/or can contain linear approximations to non-linear relationships, e.g., production functions of the Cobb-Douglas type. Best, Bernhard -----Urspr?ngliche Nachricht----- Von: Berend Hasselman [mailto:bhh at xs4all.nl]

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-