similar to: Statistical test for stationarity-how

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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 =

Hi there, Does anyone know if R has a function for testing whether a time series is stationary?? Thanks in advance, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James's Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St James's

Hi, I have an xts starting with a number of columns (currency pairs see below), then I add new ones which are derived from existing ones (like adding the moving average of a column) by merging the new columns one by one. These get the name of the column they are calculated from concatenated with ".1". All done by merge.xts, easy. Now, I have a function (procState below) which generates

How can I make sure the residual signal, after subtracting the trend extracted through some technique, is actually trend-free ? I would greatly appreciate any suggestion about some Stationarity tests. I'd like to make sure I have got the difference between ACF and PACF right. In the following I am citing some definitions. I would appreciate your thoughts. ACF(k) estimates the correlation

Hi all, Can anyone clear my doubts about what conclusions to take with the following what puts of some time series tests: > adf.test(melbmax) Augmented Dickey-Fuller Test data: melbmax Dickey-Fuller = -5.4075, Lag order = 15, p-value = 0.01 alternative hypothesis: stationary Warning message: p-value smaller than printed p-value in: adf.test(melbmax)

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

This is not exactly an R question but the R code below may make my question more understandable. If one plots sin(x) where x runs from -pi to pi , then the curve hovers around zero obviously. so , in a"stationary in the mean" sense, the series is stationary. But, clearly if one plots the acf, the autocorrelations at lower lags are quite high and, in the "box jenkins"

I'm trying to understand how to use kpss.test() properly. If I have a level stationary series like rnorm() in the help page, shouldn't I get a small p-value with the null hypothesis set to "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 <-

I have a problem with expressions. I am trying to create a title where the parameter of interest is displayed as a Greek character. Which parameter is being considered is stored in a character variable. As an example, if I have param <- "alpha" and then do plot(0, 0, main = bquote(Parameter==.(param))) then in the title I get "Parameter = alpha", whereas I want the

Hi all, Hope you people do not feel irritated for repeatedly sending mail on Time series. Here I got another problem on the same, and hope I would get some answer from you. I have following dataset: data[,1] [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 4.98 4.99 4.99 5.00 5.01 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03

is that 'x' is level or trend stationary. I did this > s<-rnorm(1000) > kpss.test(s) KPSS Test for Level Stationarity data: s KPSS Level = 0.0429, Truncation lag parameter = 7, p-value = 0.1 Warning message: p-value greater than printed p-value in: kpss.test(s) My question is whether p=0.1 is a good number to reject N0? On the other hand, I have a

I am investigating some approaches to reproducible research. I need in the end to produce .html or .doc or .docx. I have used hwriter in the past but have had some problems with verbatim output from R. Tables are also not particularly convenient. I am interested in R2HTML and R2wd in particular, and possibly odfWeave. Does anyone have sample documents using any of these approaches which

I have been struggling trying to write some code to produce all combinations subject to some restrictions. I thought someone might have some bright ideas. I have 11 values which fall into 5 groups. I want all combinations of 2,3, and 4 values where each value must be from a different group. The numbers in the groups are different. Here is a definition of the groups: groups <- list(gp1 =

Hi All, First of all, could you tell me what the "KPSS Level" in the output of the test means? I have a series, x, of periodic data and tried kpss.test() on it to verify its stationarity. The tests gave me the p-value above 0.1. Since the null hypothesis N0 is that the series _is_ stationary, this means that I cannot reject N0. But the series does look periodic! So does all this

Can someone explain to me in what way the new (dpqr)gamma parameter can be interpreted as a rate (when shape != 1)? The only gamma rate that I am aware of is the hazard rate given by dgamma/(1-pgamma), the log of which is returned by my hgamma function (event library). Jim -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

---------- Forwarded message ---------- From: Velappan Periasamy <veepsirtt at gmail.com> Date: Sat, Aug 7, 2010 at 11:20 PM Subject: quantmod Example-google data download-problems To: r-sig-finance at stat.math.ethz.ch getSymbols("YHOO",src="google") is working getSymbols("NSE:RCOM",src="google") is not working. then how to download the stock data

Hi all, Could anybody tell me how to test for stationarity in time series? Thanks a lot! [[alternative HTML version deleted]]

Does anyone know of a test for stationarity of a time series, or like all ordination techniques it is a qualitative assessment of a quantitative result. Books, papers, etc. suggestions welcome. thanks Stephen -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are

Dear R list members, Please excuse my ignorance but as a new comer to R I was wondering if anyone knows of any functions in R or Splus that can test a time-series for non-stationarity such as the Pettitt or the Mann-Kendall tests. Kind regards, Jon Nott [[alternative HTML version deleted]]