similar to: Time Series - ARIMA differencing problem

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

Dear R, I have find a website where they report problem with ARIMA models in R. I run the examples there and they give result as shown on the website. Does this mean that nothing has corrected in R? Maybe you not have seen the page, but the author said he contacted you. Here is the URL: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm I like to know your opinion. Mvh. Marie [[alternative

Dear R, I have find a website where they report problem with ARIMA models in R. I run the examples there and they give result as shown on the website. Does this mean that nothing has corrected in R? Maybe you not have seen the page, but the author said he contacted you. Here is the URL: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm I like to know your opinion. Mvh. Marie [[alternative

Hi, My query is related to ARIMA function in stats package. While looking for the time series literature I found following link which highlights discrepancy in "arima" function while dealing with differenced time series. Is there a substitute function similar to "sarima" mentioned in the following website implemened in R? Any pointers would be of great help.

I have written before, but to no avail. I have found two minor problems with fitting time series models with R. The thing is, they may be solved with MINOR adjustments to the code. I have posted these problems with detailed examples here: http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm Briefly, the problems are (1) When fitting time series models when there is an AR term present, the

Hi all, I have two time series that I would like to correlate but as they are autocorrelated, I am "pre-whitening" them first by fitting ARIMA models, then correlating their residuals....as described in https://onlinecourses.science.psu.edu/stat510/?q=node/75 However, http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm discusses some issues with ARIMA in R. In particular, for issue 2, if

David Stoffer describes some challenges with R's output when fitting ARIMA models for different orders (see Issue 2 at http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm). R doesn't fit an intercept in the model if there is any differencing. David describes a workaround using the xreg parameter to force R to calculate an intercept. Assume I have a variable y and 3 explanatory variables a,

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

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u

Hi, I have a suggestion for the fonction arima and arima0. I think you should not call the constant an intercept because it creates confusion. It is not really an intercept but a mean. For an AR(1) the intercept mu should be defined as: X(t)=mu + phi X(t-1) + e(t) What you call intercept mu is rather defined as (X(t)-mu) = phi (X(t-1)-mu)) + e(t) which is not a common way to define an

Hi all, I would like to know whether there is any function in R were i can find the cross-correlation of two or more multivariate (time series) data. I tried the function ccf() but it seems like to have two univariate datasets. Please let me know. sincerely, sandeep -- Sandeep Joseph PhD Post Doctoral Associate Center for Tropical & Emerging Global Diseases Paul D. Coverdell Center,

Hello,Could somebody tell me what is the difference between  theese 3 calls of functionsarma(x,order=c(1,0)), arima(x,order=c(1,0,0)) ar(x,order=1)?I expected same residuals of theese three models,but unexpectably for the first two R requiredinitial value of something (what?)...Thanks in advance!  [[alternative HTML version deleted]]

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

Most of the items on this list have been mentioned before but it may be useful to see them altogether and at any rate every year I have posted my R wishlist at the beginning of the year. High priority items pertain to the foundations of R (promises, environments) since those form the basis of everything else and the foundation needs to be looked after first. The medium items are focused on

Hello all, I''m having fun again with the arima function. This time I read in: http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm <<It has recently been suggested (by a reliable source) that using xreg in arima() does NOT fit an ARMAX model [insert slap head icon here]. This will be investigated as soon as time permits.>> (by R.H. Shumway & D.S. Stoffer)

Hi All, I have a very simple question about a time series object: how to access values for a particular year and quarter (say)? Suppose, following http://www.stat.pitt.edu/stoffer/tsa2/R_time_series_quick_fix.htm I have read in data as a time series; here is how it looks. * Qtr1 Qtr2 Qtr3 Qtr4 1960 0.71 0.63 0.85 0.44 1961 0.61 0.69 0.92 0.55 . . . . .

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

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]]

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

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