similar to: Time Series Issues, Stationarity ..

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

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

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

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

I have been trying to import a csv file to r. but I get the same message everytime. the message is Error in file(file, "rt") : cannot open the connection In addition: Warning message: In file(file, "rt") : cannot open file 'Users:/sezginozcan/Downloads/beer.data.csv': No such file or directory I use mac. I tried this command also

For example, based on a certain condition, I may want to exit my code early: # Are there the same number of assets in "prices" and "positions"? if (nAssetPositions != nAssetPrices) { cat("Different number of assets! \n\n") <exit function> } I have searched, but not

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]]

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

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

Hi All, I have hunted high and low and tried dozens of things but have yet to achieve the result I require. Below is my code (taken mostly from another thread on here) thus far: files<-list.files() files<-files[grep('.wm4', files)] labels<-gsub('.wm4', '',files) for(i in 1:length(files)){ X<-read.table(files[i]) <My problem is here!> What I am trying

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)

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 <-

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

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

My Java servlets can generate both JavaScript objects, like JSON, and XML. I can generate both pretty easily. What''s best practice for AJAX responses? Return scripts to be evaled by the Ajax control, or return XML documents and iterate them using XML DOM? Thanks. -- Alan Gutierrez - alan@engrm.com - http://engrm.com/blogometer/index.html -

Hi, I have many .txt files which look like this: 2009/02/07 12:30:10.0 ? ? ?5.0161 ? ? ?13.208 2009/02/07 12:45:10.0 ? ? ?5.0102 ? ? ?13.350 2009/02/07 13:00:10.0 ? ? ?5.0044 ? ? ?13.473 .... .... .... 2009/02/07 16:30:10.0 ? ? ?4.9366 ? ? ?13.788 2009/02/07 16:45:10.0 ? ? ?4.9397 ? ? ?13.798 end data. ###I can read in all files from "my_folder" using the following code: