search for: differenced

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 doesn't exist when using a model with differencing. In this c...

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]]

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

Full_Name: Arto Luoma Version: 1.1.0 OS: Windows 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...

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). I think when modeling diff(y) the coefficients

...rary 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 <- c(750, 775, 971, 1099, 1344, 610, 910, 1056, 1589, 1006, 1469, 1598, 876, 1104, 1197, 1062, 1783, 1554, 1400, 1340, 1013, 1031, 1030, 860, 943, 1542, 1400, 1301, 1443, 1057, 1296, 710, 1038, 1447, 694, 889, 1459, 957, 1284, 1520, 1172, 1826, 1751, 1968, 933, 1409, 1889)...

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

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

How does the R module ARIMA account for unspecified deterministic structure such as seasonal pulses, level shifts, local time trends and regular pulses without needing to ask the user to intervene to specify this? I have attached a Makradakis paper which hammers Box-Jenkins approach to this problem of nonstationarity. I have also included a recent discussion from stackexchange which you might

...using meanobs<-aggregate(ctd[,7:9],by=list(EventNum=EventNum),mean) But, I want to calculate a depth-weighted mean for salinity, temp and oxygen for each EventNum. The depth-weighted mean is calculated as Xbar = Sum{depth_interval*temp}/Sum(depth_intervals) Where the depth interval is the differenced depths (i.e., 0.103-0.056 for the second row of data). I cannot seem to get diff to work the way I want it to - advice appreciated Tom Thomas J. Miller, Ph.D. Professor Chesapeake Biological Laboratory University of Maryland Center for Environmental Science Solomons, MD 20688 Tel: (410) 326-727...

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/**/" \

...commit changes ... after also adding a proper > regression test. > Be careful here! I was just about to say that the diagnosis is dubious, and that the patch could very well be wrong!! AFAICT, the issue is that n.used got changed from being based on lm(x~...) to lm(dx~...) where dx is the differenced series. 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...

...er >>> regression test. >>> > >> Be careful here! I was just about to say that the diagnosis is dubious, and that the patch could very well be wrong!! > >> AFAICT, the issue is that n.used got changed from being based on lm(x~...) to lm(dx~...) where dx is the differenced series. 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. > > well... yes, but as you say for the case of the original lm(...

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

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

...eries 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 "prediction$pred" to the level data since it has been differenced once? I know exp (prediction$pred) will bring the inverse of the log transform but what about the differenced transform? This is my question. I would be very grateful if you could help me with this.Thank you very much in anticipation Mr. Mahmoud Coker Senior Manager Bank of Sierra Leone (Sam-Ban...

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...

(new) blktap-dev_2.0.90-1_amd64.deb extra devel Xen API blktap shared library (development files) The Xen Cloud Platform (XCP) is an open source enterprise-ready server virtualization and cloud computing platform, delivering the Xen Hypervisor with support for a range of guest operating systems. This package is part of it. It implements the userland part of the blktap driver. . Virtual Hard

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.

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) #