search for: stationarity

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: 0.13...

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...

Hi, How can I access the Values stored in kpssstat-class given by KPSS.test function and store it in a variable. For example: >x <- rnorm(1000) >test <- KPSS.test(ts(x)) >test ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null hypothesis of level stationarity: 0.138 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: 0.03...

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

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]]

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 between y(t) and y(t-k) like an ordinary correlation coefficient. ACF is the simple ( i.e. unco...

...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 > melbmaxr...

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 say stationarity and periodicity can co-exist? Thanks, Weiguang

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

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 series r and did the following: > plot.ts(r)...

...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 t...

How to find out if EURGBP is stationary? Post the R codes for the same.

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 =

...f (tonal->highE[b] > tonal->lowE[b] + 7.5f) { if (tonal->highE[b] - logE[b] > logE[b] - tonal->lowE[b]) tonal->highE[b] -= .01f; @@ -700,7 +700,7 @@ static void tonality_analysis(TonalityAn L2 += tonal->E[i][b]; } - stationarity = MIN16(0.99f,L1/(float)sqrt(1e-15+NB_FRAMES*L2)); + stationarity = MIN16(0.99f,L1/(float)sqrt(1e-15f+NB_FRAMES*L2)); stationarity *= stationarity; stationarity *= stationarity; frame_stationarity += stationarity; @@ -748,7 +748,7 @@ static void tonality_analysis(Tonal...

...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 printed...

...z-score p-value Z-Score p-value b[1] 1.143 0.253 0.000 1.000 b[2] 0.470 0.638 0.056 0.956 b[3] -1.037 0.300 -0.388 0.698 b[4] -0.618 0.536 -0.085 0.933 tau -0.206 0.837 -0.008 0.994 sigma 0.716 0.474 0.437 0.662 CODA -- Heidelberger and Welch Stationarity start p-value test iteration b[1] passed 1 0.2649 b[2] passed 1 0.6709 b[3] passed 1 0.6376 b[4] passed 1 0.3673 tau passed 1 0.1944 sigma passed 1 0.0725 Halfwidth Mean Halfwidth...

...276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287, 267, 267, 288, 304, 273, 264, 254, 263, 265, 278) x <- ts(x, frequency = 12) library (urca) library (uroot) library (tseries) Now, doing an ur.kpss test (mu, lag=3) i cannot reject the null hypothesis of level stationarity. Doing a kpss.test (mu, lag=1 by default ) the p value becomes smaller than 0.05 thus rejecting the null of stationarity. Same with KPSS.test (lag=1) So, as i have noticed that increasing the number of lags on each of the tests rejecting the null becomes harder and harder. I saw that books always...

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

Hi I am thinking of ripping albums to a single flac file with embedded cuesheet. As track and index points have to be on a 588 sample boundary due to the CD TOC standard working in 588 sample frames, I thought it may be beneficial to rip CDs with a blocksize of 588 samples. According to the format page on sourcefourge a stream is subset if "The blocksize bits in the frame header must be

hello R experts, my question is regarding arma modelling and specification. in another older, statistics package , after determining stationarity, i would try to work out the number of ar and ma lags using an lm test. to do this i would 1. regress my dependant variable on an intercept term then 2. use LM test for serial correlation, and finally 3. use the p value of the ols residuals to get the maximum lags for the arma specification. I...