similar to: extracting response from arima obj

hi all, In a simulation context I'm running in a for loop the arima() function for( i in 1:1000){ y<-arima.sim(....) out<-arima(y,....) ........ } Everything works, but after some cycle (10, say) I get error due to the particular y-values simulated. (E.g., a *frequent* error is "Error in svd(na.omit(xreg)) : 0 extent dimensions") As a

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

Hi, Does anyone know a method for adding a linear/polynominal trend to a simulated arima model using the arima.sim function? Any help will be greatly appreciated. Cheers, Sam.

Dear List, A posting to R-Help exposed this problem with the print method for objects of class Arima: > set.seed(1) > x <- arima.sim(n = 100, list(ar = 0.8897, ma = -0.2279)) > mod <- arima(x, order = c(1,0,1)) > coefs <- coef(mod) > mod2 <- arima(x, order = c(1,0,1), fixed = coefs) > mod2 Call: arima(x = x, order = c(1, 0, 1), fixed = coefs) Coefficients: Error

Full_Name: Allan McRae Version: 1.6.0 OS: Win 2000 P Submission from: (NULL) (129.215.190.229) When using predict.Arima in library ts(), it appears differencing is only accounted for in the first step of prediction and so any trend is not apparent in the predictions. The example shows the difference between the predictions of an arima(1,1,1) model and the backtransformed predictions of an

Dear all, I would like to write a function like: myfun<-function(x,fn) {xx<-exp(x); x*fn(xx)} where fn is a symbolic description of any function with its argument to be specified. Therefore myfun(5,"2+0.3*y^2") should return 5*(2+0.3*exp(5)^2), myfun(5,"log(y)") should return 5*log(exp(5)) and so on. I tried with "expression" and others, but without success.

Hi, I am trying to forecast a model using predict.Arima I found arima model for a data set: x={x1,x2,x3,...,x(t)} arima_model = arima(x,order=c(1,0,1)) I am forecasting the next N lags using predict: arima_pred = predict(arima_model,n.ahead = N, se.fit=T) If I have one more point in my series, let's say x(t+1). I do not want to recalibrate themodel, I just want to forecast the next N-1

Hi, I am trying to understand exactly what xreg does in arima. The documentation for xreg says:"xreg Optionally, a vector or matrix of external regressors, which must have the same number of rows as x." What does this mean with regard to the action of xreg in arima? Apparently somehow xreg made the following two arima fit equivalent in R: arima(x, order=c(1,1,1), xreg=1:length(x)) is

Dear all, When I run an arima(1,1,1) on an I(1) variable, y, I get different estimates to when I first difference the variable myself, e.g y2<-diff(y), and then run arima(1,0,1) on y2. Shouldn't these two approaches give the same output? Any help will be much appreciated. george

Hi, I'm using the function arima() from the ts package. when the function gives me the output I can see the s.e. of the coefficients. However I cannot find a way to collect them in a object estimate<-arima(x, order=c(1,0,1)) estimate$se does not work in fact str(estimate) does not contain $se. How is it possible? The function compute and print that value but it is not the resulting

It works!!! Thank you so much for your help! Sent from my iPhone > On Feb 3, 2020, at 3:47 AM, Rui Barradas <ruipbarradas at sapo.pt> wrote: > > ?Hello, > > You can solve the problem in two different ways. > > 1. Redefine storage1 as a matrix and extract the aic *in* the loop. > > storage1 <- matrix(0, 4, 4) > for(p in 0:3){ > for(q in 0:3){ >

Here is my problem: Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc). Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc). arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation)

Hello I am trying to extract AICs from an ARIMA estimation with different combinations of p & q ( p =0,1,2,3 and q=0,1.2,3). I have tried using the following code unsucessfully. Can anyone help? code: storage1 <- numeric(16) for (p in 0:3){ ? ? for (q in 0:3){ ? ? ? storage1[p]? <- arima(x,order=c(p,0,q), method="ML")} } storage1$aic [[alternative HTML version deleted]]

Hi, i'm beginner in Bayesian methods, I'm reading the documentation about dlm package and kalman filters, I'm looking for a example of transformation of ARIMA in a state space equivalent to use the dlm package and calcualte the hyperparameters. Someone can help me about it?. If it's possible with a arima(1,0,1) example, or more complex model. While I have more examples best for me.

I am nowaware that I should not post this type of questions on this group. However, Iwould like to have some clarifications related to the response you've?alreadyprovided. The code you provided yields accurate results, however I still haveissues grasping the loop process in case 1 & 2. In case1,?the use of?"p+1" and "q+1" is still blurry tome? Likewise "0L"

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 ) {

I would like to simulate some SARIMA models, e.g. a SARIMA (1,0,1)(1,0,1)[4] process. I installed the package 'forecast', where the function simulate.Arima should do what I am trying to do. I am not able to understand how it works Could somebody help me with an example? thank you Stefano Sofia AVVISO IMPORTANTE: Questo messaggio di posta elettronica pu? contenere informazioni

dear all, Given a matrix A, say, I would like to apply a bivariate function to each combination of its colums. That is if myfun<-function(x,y)cor(x,y) #computes simple correlation of two vectors x and y then the results should be something similar to cor(A). I tried with mapply, outer,...but without success Can anybody help me? many thanks in advance, vito

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

Dear R community, I have a project with electricity load forecasting, and I got hourly data for system load. If you haven't worked with electricity before, seasonality comes in many flavors: a daily pattern, with a peak at around 7pm; a weekly pattern, in which we use more electricity on weekdays in comparison to weekends; a winter-summer pattern, with air conditioning and heaters playing an