search for: differencing

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 origin...

Hi all, I was wondering how to construct a seasonal differenced time series variable. I used the following code to construct a 12 span seasonal difference seasonal<-diff(V2, lag=12, differences=1) is this correct? thank you in advance joe [[alternative HTML version deleted]]

...,0) to it, then this gives the correct coefficients but the forecasts are wrong (ie they are flat and do not follow the trend). Could anyone think of a way to get both the coefficients AND the forecasts correct? Thanks. -- View this message in context: http://www.nabble.com/Time-Series---ARIMA-differencing-problem-tp22353903p22353903.html Sent from the R help mailing list archive at Nabble.com.

...ndows 98 Submission from: (NULL) (153.1.53.119) In arima0 it is possible to specify whether the mean of the original series is included in the model or not. However, it is not possible to specify whether the mean of the differenced series is included. It seems that it is not included. However, if differencing is used to eliminate trend, the mean of the differenced series is not zero. In my opinion, this leads to errors both in estimation and prediction problems. The uncertainty in the estimation of the mean of the differenced series should be also included in the standard errors of prediction. -....

I am having some issues with differencing using auto.arima when also specifying an xreg dataframe. The xreg dataframe contains dummy variables that specify time periods that had a promotion running. When I model diff(y) with order (1,0,1), the coefficients for these dummy variables are very different than when I model y with order=(1,1,1...

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 arima(1,0,1) model fitted on the differenced data. Example: total...

Hi, I have been using this site ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm) to help me with some ARIMA modelling in R. Unfortunately the methods mentioned do not appear to work with second order differencing; arima(*, 2, *). I have used some dummy data to illustrate my point. When I use the xreg=... method, the estimate of intercept is *way* off. This can be seen by the high s.e but I *have* checked it in SPSS. This in turn gives the wrong t and p values (using the data that I'm actually working...

Hi! I'm using libvirt withVirtual Box. I have installed libvirt 0.10.2 and Virtual Box 4.1.Is possible to spesify the pat to the folder I want my differencing image created in in my domain.xml file? When I define my vm (virsh domain.xml) with my domain.xml and use the readonly tag, Virual Box creates a differencing image (default in users Vitual Box VMs folder). I want to specify in my domain.xml file what folder this differencing image should be created...

....stackexchange.com/questions/12651/box-jenkins-model-selection/12662#12662 http://www.insead.edu/facultyresearch/research/doc.cfm?did=46900 ----- Tom Reilly www.autobox.com -- View this message in context: http://r.789695.n4.nabble.com/Discussion-on-time-series-analysis-and-the-use-and-misuse-of-Differencing-tp3652268p3652268.html Sent from the R help mailing list archive at Nabble.com.

Colleagues, I am analyzing data collected during oceanographic cruises. We have conducted many cruises over the last decade. On each cruise we visit ~50 stations. At each station (termed EventNum)we lower an instrument that measures depth, temperature, salinity and oxygen every few seconds as it is lowered through the water. Data from all EventNums on all cruises are stacked, generating a data

I'm finding a fairly major difference in the behavior of include/exclude between 2 version of rsync: opensolaris running rsync version 2.6.9 Cygwin running rsync version 3.0.4 Run from Opensolaris using version 2.6.9 and pulling from a windows XP box with this cmdline: (Note: cmds are wrapped for mail) rsync -nvviirp --inplace --include="Temp/**/" \

...Now that surely loses one observation in arima(.,1,.), most likely unintentionally, but it is not at all clear that the fix is not to subtract length(Delta) -- that code has been there long before the changes in 3.0.2. I'd expect that a safer fix would be to add back the orders of the the two differencing operations. > Martin Maechler, ETH Zurich -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Office: A 4.23 Email: pd.mes at cbs.dk Priv: PDalgd at gmail.com

...there long before the changes in 3.0.2. > > well... yes, but as you say for the case of the original lm() > fit where the resulting residuals and hence is.na(resid(.)) have > been longer.... > >> I'd expect that a safer fix would be to add back the orders of the the two differencing operations. > > What I did check before replying is that the patch *does* revert to 'R <= 3.0.1' > behavior for simple 'xreg' cases. > > I do see changes in the S.Es of the regression coefficients, as > they are expected. > > The few cases I've l...

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 https://bugs.r-project.org/bugz...

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 = TRUE ... : non-finite fin...

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

Hi I'm using fracdiff package to estimate the parameters of a fractionally-differenced ARIMA (p,d,q) model, and it works fine, but I wanted to have also the filtered series and the series of residuals. I understand these are calculated in the subroutine fdfilt, in the program fdcore.f, but I can't manage to get them out. Any suggestion would be much appreciated Thanks Susana Barbosa

...types, such as file systems or logical volumes. . VHDs are container files storing block metadata alongside a virtual disk's contents, in a single disk image. The metadata enables advanced storage backend capabilities, such as thin provisioning, disk image snapshotting, and coalescing of differencing disks. . Libvhd is a library to access VHD format metadata. It provides disk image initialization, inspection and manipulation to both VHD user utilities and the VHD disk driver in Blktap. . This package contains the development files (new) blktap-utils_2.0.90-1_amd64.deb extra utils utiliti...

...: 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. plot(m1.dis,n1=c(1990,1),n.ahead=36,xlab='Year',type='o',ylab='Discharge (cfs)',transform=exp) [[alternative HTML version deleted]]

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) # remains autocorrelated...