search for: stationari

...terms of its acf. so, i'm confused in terms of what ithe statistical definition of stationary is as box jenkins define it ? I don't have their text in front of me but I don't remember them having an example such as below when they talk about needing to difference series to achieve stationarity. thanks for any insights or a text that talks about this. x <- seq(pi,-pi,by=-pi/4) y <- sin(x) plot(x,y) acf(y) P.S: this question arose because a colleague asked me to look at the plot of his series and the associated acf and he claims it's a stationary series and I'm trying...

...melbmax) Box-Pierce test data: melbmax X-squared = 1893.093, df = 1, p-value < 2.2e-16 > Box.test(melbmax,type="Ljung-Box") Box-Ljung test data: melbmax X-squared = 1894.650, df = 1, p-value < 2.2e-16 > kpss.test(melbmax) KPSS Test for Level Stationarity data: melbmax KPSS Level = 0.1163, Truncation lag parameter = 13, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(melbmax) > x=time(melbmax) > y=as.vector(melbmax) > melbmaxsaz=lowess(x,y,f=0.05)$y > melbmaxtrend=lowess(x,y,f=0.5)$y > melbma...

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 =

Short question: Is it possible to use statistical tests, like the Augmented Dickey-Fuller test, in functions with for-loops? If not, are there any alternative ways to scale measures? Detailed explanation: I am working with time-series, and I want to flag curves that are not stationary and which display pulses, trends, or level shifts. >df DATE ID VALUE2012-03-06 1

...0 5.20 5.20 5.21 5.21 5.21 5.21 5.21 5.21 5.22 5.22 5.23 [127] 5.23 5.23 5.23 5.24 5.24 5.24 5.25 5.24 5.24 5.25 5.26 5.26 5.26 5.26 5.26 5.26 5.26 5.27 [145] 5.27 5.26 5.27 5.27 5.28 5.29 5.29 5.29 5.29 5.30 5.30 5.30 5.31 5.31 5.31 5.32 5.32 5.33 [163] 5.33 Now I want to conduct a test for stationarity using ADF test : > adf.test((data[,1]), "stationary", 0) Augmented Dickey-Fuller Test data: (data[, 1]) Dickey-Fuller = -3.7351, Lag order = 0, p-value = 0.02394 alternative hypothesis: stationary But surprisingly it leads towards rejestion of NULL [p-value i...

Hi, Am I interpreting the results properly? Are my conclusions correct? > KPSS.test(df) ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null hypothesis of level stationarity: 1.089 Critical values: 0.10 0.05 0.025 0.01 0.347 0.463 0.574 0.739 ---- Statistic for the null hypothesis of trend stationarity: 0....

Hi ! Sorry for my ignorance. I have two questions: 1) which is in R the function to make a covariogram of spatial data ? 2) does anyone know if there exist the possibility in R of performing kriging with an arbitrary covariance function ? If I have well understood the Krig procedure offers only three possibilities (expcov, gauscov and sphercov) that are the common models for stationary and

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 =

Hi, I am trying to test whether a series is return series stationary, but before proceeding I wanted to make sure I understand correctly how to use the adf.test function and interpret its output... Could you please let me know whether I am correct in my interpretations? ex: I take x such as I know it doesn't have a unit root, and is therefore stationary 1/ > x <- rnorm(1000) >

Hi all, I was running the adf test in R. CODE 1: adf.test(data$LOSS) Augmented Dickey-Fuller Test data: data$LOSS Dickey-Fuller = -1.9864, Lag order = 2, p-value = 0.5775 alternative hypothesis: stationary CODE 2: adf.test(diff(diff(data$LOSS))) Augmented Dickey-Fuller Test data: diff(diff(data$LOSS)) Dickey-Fuller = -6.9287, Lag order = 2, p-value = 0.01 alternative

Hello! One note/question hier about specification of control-parameters in the lme(...,control=list(...)) function call: i tried to specify tne number of iteration needed via lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE)) but every time i change the defualt values maxIter (e.g. maxIter=1, niterEM=0) on ones specified by me, the call returns all the iterations needed until

Hi All, I am a newcomer to R. Could anyone explain me how to define link functions for either mu/sigma to allow for quadratic trends in the same, when fitting non-stationary GEV distributions? Thanks -- View this message in context: http://www.nabble.com/QUADRATIC-TREND-FOR-LINK-FUNCTIONS-ON-NON-STATIONARY-GEV-tp23360751p23360751.html Sent from the R help mailing list archive at Nabble.com.

Hi, I have a sample of size 100 from a function in interval [0,1] which can be assumed to come from a scalar-valued stationary Gaussian process. There are about 500 observation points in the interval. I need an effective and fast way to simulate from the Gaussian process conditioned on the available data. I can of course estimate the mean and 500x500 covariance matrix from data. I have searched

...quot;Trend"? The (condensed) output from kpss.test() for the two possible null hypotheses is given below. I don't see any significant difference between these results. > x <- rnorm(1000) # is level stationary > kpss.test(x, null="Level") KPSS Test for Level Stationarity KPSS Level = 0.0638, Truncation lag parameter = 7, p-value = 0.1 Warning: p-value greater than printed p-value > kpss.test(x, null="Trend") KPSS Test for Trend Stationarity KPSS Trend = 0.0275, Truncation lag parameter = 7, p-value = 0.1 Warning: p-value greater than print...

...o find the starting values and then ML (as described on the help page). Now using the default may lead to error messages saying: "non-stationary ar part in CSS". When changeing the default to "ML" only the minimization works. As far as I understand, arima doesn't require stationarity, but apparently CSS does. Can anyone tell me what exactly the css method does? And why is CSS-ML the default in R? Out of efficiency reasons? Because ML and ML-CSS gives the exact same estimates when applied to the same data. I tried to find out on google but I couldnt' find anything usefu...

Hi All, I am a newcomer to S/R. Could you please let me know how to model quadratic trends for the mul/sigl link functions when fitting non-stationary GEV distributions using the ismev package? Thanks Best Regards, Mohammad Ashrafuz Zaman PhD Candidate School of Engineering Building XC, Room 1.02 (Kingswood Campus) University of Western Sydney Locked Bag 1797, Penrith South DC NSW 1797

Dear R experts,   I'm running the following stationary bootstrap programming to find the parameters estimate of a linear model:     X<-runif(10,0,10) Y<-2+3*X a<-data.frame(X,Y) coef<-function(fit){   fit <- lm(Y~X,data=a)    return(coef(fit)) }  result<- tsboot(a,statistic=coef(fit),R = 10,n.sim = NROW(a),sim = "geom",orig.t = TRUE)   Unfortunately, I got this

Dear R users, I have a function (below) which encompasses several tests. However, when I run it, only the output of the last test is displayed. How can I ensure that the function root(var) will run and display the output from all tests, and not just the last one? Thank you, b. root <- function(var) { #---Phillips-Perron PP.test(var, lshort = TRUE) PP.test(var, lshort = FALSE)

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

Dear all, In many papers regarding time series analysis of acquired data, the authors analyze 'marginal distribution' (i.e. marginal with respect to time) of their data by for example checking 'cdf heavy tail' hypothesis. For i.i.d data this is ok, but what if samples are correlated, nonstationary etc.? Are there limit theorems which for example allow us to claim that