similar to: how to make ARFIMA forecast by using r?

Hello. I'm trying to make k-step-ahead forecasts using ARFIMA(0, d, 0) models by taking the first T+k-1 coefficients in the binomial expansion of (1-B)^d, regarding (1-B)^d x(T+k) as an AR(T+k-1) on x(T+k), where x(T) is the series value at time T and k = 1, 2, 3, . That is, I forecast the series k values forward using the first T+k-1 coefficients in the binomial expansion of (1-B)^d as

>>>>> 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

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

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]]

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:

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]]

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]]

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

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

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

R gurus, I've exhausted my search of online help. This is my last resort. We're running R-2.1.1. I've been told by one of the R users here that we cannot upgrade to the lastest version because of some python rpy wrapper dependency that hasn't caught up to the latest version of R. The software is running on Solaris 10 x86. Gcc version is 3.4.1. R is installed in

Hi all, Does anyone have the knowledge to help me identify a package capable of forecasting a MULTIPLE regression model? i have a model with one one dependant variable and 4 independant variables. i would like to forecast confidence intervals for a few steps ahead...(DENSITY forecasting). PS i can forecast a univariate vector, with package 'forecast', however, i want one that can do

Hello, I am using forecast() in the forecast package to predict future values of an ARIMA model fit to a time series. I have read most of the documentation for the forecast package, but I can't figure out how to obtain the forecast variance for the predicted values. I tried using the argument "se.fit=TRUE," hoping this would work since forecast() calls predict(). Is there an easy

I've some demand data, for which I wish to judge what will be the direction in the forecast period (Up/Down). What will be a best ML method to do this? Currently I'm using the data given below - 9/4/2016 241 9/11/2016 233 9/18/2016 226 9/25/2016 282 10/2/2016 291 10/9/2016 282 10/16/2016 308 10/23/2016 291 10/30/2016 268 11/6/2016 262 11/13/2016 273 11/20/2016 262 11/27/2016 309 12/4/2016

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,