similar to: coding problems

Hello, I'm fitting a series of ARIMA models to a data set to compare fits. After taking the logs of the data and then differencing them to induce stationarity, I execute arima( y, order=c( p, 0, q ), seasonal=list( order=c( P, 0, Q ), period=7 ) ) for various values of p, q, P and Q. For one set of these values, I get Error in optim(init[mask], armafn, method = "BFGS", hessian

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Good evening! I have a question regarding forecast package and time series analysis. My syntax: x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287, 267, 267, 288, 304, 273, 264, 254, 263, 265, 278) library(forecast) arima(x, order=c(1,1,2),

I don't think arima works exactly the way one would expect when there is differencing. What I think should happen is that by default the mean of the differenced series is estimated and if include.mean=F, then it is not. This is not what happens. Instead when there is differencing the include.mean argument is ignored. Now I guess, someone could argue that the mean of the original series

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

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

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

There is currently a bug in the arima function. Namely, for arima models with differencing or seasonal differencing, the innovation variance estimator uses the wrong denominator whenever xreg is non-null. This is the case, for example, when fitting an ARIMA(p,1,q) model with a drift term (common in financial applications). I reported the bug (and a fix) at

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

On 21 May 2015, at 12:49 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >>>>>> peter dalgaard <pdalgd at gmail.com> >>>>>> on Thu, 21 May 2015 11:03:05 +0200 writes: > >> On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: > >>>> >>>> I noticed that

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

On 21 May 2015, at 10:35 , Martin Maechler <maechler at lynne.stat.math.ethz.ch> wrote: >> >> I noticed that the 3.2.1 release cycle is about to start. Is there any >> chance that this fix will make it into the next version of R? >> >> This bug is fairly serious: getting the wrong variance estimate leads to >> the wrong log-likelihood and the wrong

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

Dear R users, someone knows how to remove auto-correlation from a frequencies time series? I've tried by differencing (lag 1) the cumulative series (in order to have only positive numbers) , but I can't remove all auto-correlation. If it's useful I can send my db. x <- # autocorrelated series new1<-cumsum(x) new2<-diff(new1,lag=1,differences = 1) acf(new2) #

Hello, here is another question, how do I generate random walk models in R? Basically, I need an AR(1) model with the phi^1 value equal to 1: Yt = c + Yt-1 + E where E is random white noise. I tried using the arima.sim command: arima.sim(list(ar=c(1)), n = 1000, rand.gen = rnorm) but got this error since the model I am generating is not stationary: Error in arima.sim(list(ar = c(1)), n =

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

I have the following model: m1.dis=arima(diff(diff(log(ts1),lag=12)),order=c(0,1,1),seasonal=list(order=c(0,1,1),period=12)) I would like to know how to plot the correct predictions in the original units because I am trying the following code but it is not working. I believe that there must be something to account for the differencing.

Hi R experts, I am trying to remove autocorrelation from Simple Moving Average time series. I know that this can be done by using seasonal ARIMA like, library(TTR) data <- rnorm(252) n=21 sma_data=SMA(data,n) sma_data=sma_data[-1:-n] acf(sma_data,length(sma_data))

I noticed that the 3.2.1 release cycle is about to start. Is there any chance that this fix will make it into the next version of R? This bug is fairly serious: getting the wrong variance estimate leads to the wrong log-likelihood and the wrong AIC, BIC etc, which can and does lead to suboptimal model selection. If it's not fixed, this issue will affect every student taking our time series

Full_Name: Gang Liang Version: 1.7.1 OS: Debian/Woody Submission from: (NULL) (192.6.19.190) > print(arima.sim(list(ar=.3,order=c(1,1,1)), 30)) [1] 0.00000000 0.10734243 0.02907301 -1.23441659 -0.98819317 -2.82731975 [7] -2.69052512 -4.22884756 -5.02820635 -5.41514613 -6.20486350 -7.01040649 [13] -6.78121289 -5.41111810 -4.96338053 -5.42395408 -6.22741444 -5.75228153 [19] -6.07346580