similar to: p-value for the parameter in ARIMA model with R

Hi I'm trying to fit arima models with the arima() function and I have two questions. ###### ##1. ## ###### I have n observations for my time series. Now, no matter what arima(p,d,q)- model I fit, I always get n residuals. How is that possible? For example: If I try this out myself on an AR(1) and calculate the fitted values from the estimated coefficients I can calculate n-1 residuals.

Dear All, Let: dp: depth of the?river tp: temperature with respect to depth We can?have a simple scatter plot, between depth as y-axis and temperature as x-axis,?by using a?plot function as shown?below. #####################? dp <- c(1,4,3,2,5,7,9,8,9,2) tp <- 1:10 plot(tp,dp, type= 'l') ##################### ? Could someone advice me on?the way to?plot the same pair of

Hello, I have a question about function arima.sim I tried to somulate a AR(1) process, with no innovation, no error term. I used this code: library(forecast) e=rnorm(100,mean=0,sd=0) series=arima.sim(model=list(ar=0.75),n=100,innov=e)+20 Then I tried to applicate ti this series auto.arima function: mod1<-auto.arima(series,stepwise=FALSE,trace=TRUE,ic='aicc') The best model returned

Dear All, Let a, b and c are?three matrices with same no. of column but different no. of row. a <- matrix(1, 1, 2) b <- matrix(2, 2, 2) c <- matrix(3, 3, 2) Could someone help me to combine these matrices together as a single matrix? Thank you Fir

Another pathological test. arima does not crash for that series that crashes arma: arima(c(2.01, 2.22, 2.09, 2.17, 2.42), order=c(1,0,0)) However, arima crashes for this: arima(c(1.71, 1.78, 1.95, 1.59, 2.13), order=c(1,0,0)) arima seems pretty consistent in its crashing behaviour, since crashing for one series means crashing for all affine series: lets.crash.arima <- c(71, 78, 95, 59,

Dear All, I was looking for the color index in?image function,?such as from topo.colors(n) and etc. but still never found it. For instance, from the help menu. ########################################### # Volcano data visualized as matrix. Need to transpose and flip # matrix horizontally. image(t(volcano)[ncol(volcano):1,]) # A prettier display of the volcano x <- 10*(1:nrow(volcano)) y

Hi, Using arima on data that are in a data frame, especially when adding xreg, would be much easier if the arima function contained 1) a "data=" argument 2) the possibility to include the covariate(s) in a formula style. Ideally the call could be something like > arima(symptome, order=c(1,0,0), xreg=~trait01*mesure0, data=anxiete) ( or arima(symptome~trait01*mesure0,

Dear All, I have a series of data in which the first column consist of a combination of date and time, for instance 17 April 2008 at 4.01pm, such data is recorded as: 4/17/2008 16:01 I'd like to seperate it into four different columns which consist of Day, Month,Year and Time, respectively. Could someone please advice me on this mater? Thank you, Fir

As I am reading ?arima, only NA entries in the argument fixed= imports. The following seems to indicate otherwise: x <- arima.sim(model=list(ar=0.8), n=100) + (1:100)/50 > t <- 1:100 > mod1 <- lm(x ~ t) > > init1 <- c(0, coef(mod1)[2]) > fixed1 <- c(as.numeric(NA), 0) > > arima(x, order=c(1,0,0), xreg=t, include.mean=FALSE, init=init1, fixed=fixed1)

Hi Everyone, I'm having a problem with arima.sim. Namely specifying inital values for the series. If I generate a random walk > vs = rnorm(100,0,1) > xs = cumsum(vs) and fit an ARIMA(1,0,0) to it > xarima = arima(xs,order=c(1,0,0)) > xarima Call: arima(x = xs, order = c(1, 0, 0)) Coefficients: ar1 intercept 0.9895 8.6341 s.e. 0.0106 6.1869 I should

Hi I have fitted an arima model to a monthly series of length 113. I have use fit <- arima(x.ts, order = c(1,0,0),seas = list(order = c(1,2,1),12, method = "CSS") I'm a little confused because the series of residual has the first 46 values equal to 0. From there the residuals are approximately while noise, numbers have a mean close to 0 and a variance value. What could be

Hi I am fitting an arima model to some time series X. When I was comparing the fitted values of the model to the true time series I realized that the true time series lags one time step behind the fitted values of the arima model. And this is the case for any model. When I did a simple linear regression using lm to check, I also find the same results, that the true series lags behind the

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

Dear All, Could anyone?give me a hand?to suggest few packages in R to running Kalman prediction and filtration ? Thanks Fir

I've been trying to get comfy with arima() and associated functions in the ts() package. I'm thinking seriously about using this package, and R generally, in a 4th year intro time series course that I'm teaching this autumn. I have a couple of questions about arima: (1) The help file says that residuals component of the value returned by arima() consists of 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

Dear R People: Here is some output from AR and ARIMA functions: > xb <- arima.sim(n=120,model=list(ar=0.85)) > xb.ar <- ar(xb) > xb.ar Call: ar(x = xb) Coefficients: 1 0.6642 Order selected 1 sigma^2 estimated as 1.094 > xb.arima <- arima(xb,order=c(1,0,0),include.mean=FALSE) > xb.arima Call: arima(x = xb, order = c(1, 0, 0), include.mean = FALSE)

Hi, I am using the arima.sim function to generate some AR time series. However, the function does not seem to produce exactly the same time series when I specify the innov parameter. For example > r <- rnorm(300) > x <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) > y <- arima.sim(300, model=list(order=c(1,0,0),ar=c(.96)), innov=r, n.start=10) >

i am new to time series i found in help about arima arima(x = data, order = c(p, d, q)) what is exactly p,d,q? if i not changed them,what effects will happens? -- View this message in context: http://r.789695.n4.nabble.com/what-is-p-d-q-in-arima-function-of-time-series-tp4484368p4484368.html Sent from the R help mailing list archive at Nabble.com.

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