Error In Arima Non-stationary Ar Part From Css
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Arima Seasonal R
ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine arima r learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top How to simulate only stationary AR(1) with φ = 0.9? up vote 0 down vote favorite arima model 1 I am interesting in simulating AR(1) processes with 0.9 parameter and n = 10. The itterations should be 10000. When I was trying to run the program it gave me an error in the estimation procedure. "Error in arima(newyt, order = c(1, 0, 0)) : non-stationary AR part from CSS " My code is this: set.seed(1123) rep<-10000 phix<-0.90 t<-c(NA,rep) tstar<-c(NA,rep) tdstar<-c(NA,rep) r<-c(NA,rep) cut.est<-c(NA,rep) cut2.est<-c(NA,rep) for(i in 1:rep) { ts.sim3 <- arima.sim(n = 63, list(ar=c(phix))) new<-ts(ts.sim3[c(0,14:63)]) cut.ts<-arima(new,order = c(1,0,0)) yt<-arima.sim(n = 63, list(ar=c(phix))) newyt<-ts(yt[c(0,14:63)]) cut2.ts<-arima(newyt,order = c(1,0,0)) r[i]<-cor(new,newyt) tstar[i]<-r[i]/(sqrt((((1+(phix*phix))/((1-(phix*phix)))/(50))))) t[i]<-r[i]/(sqrt((1-r[i]^2)/(50-2))) cut.est[i]<-coef(cut.ts)["ar1"] cut2.est[i]<-coef(cut2.ts)["ar1"] tdstar[i]<-r[i]/(sqrt((((1+(cut.est[i]*cut2.est[i]))/ ((1-cut.est[i]*cut2.est[i])))/(50))))) } values.t.test<-matrix(t) mean(cut.est) mean(cut2.est) values.t.test sum(abs(t) > 1.96) sum(abs(tstar) > 1.96) sum(abs(tdstar) > 1.96) Do you have any ideas? r time-series simulation error stationarity share|improve this question asked Feb 4 '14 at 9:45 harris 135 1 Try larger burn-in values. And please do not post additional code which makes so much harder to see where the problem is and which is not related to that particular problem. –mpiktas Fe
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes http://stats.stackexchange.com/questions/85374/how-to-simulate-only-stationary-ar1-with-%CF%86-0-9 a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the top Seasonal data forecasting issues up vote 0 down vote favorite 1 I have a seasonally decomposed data set. The data set has strong seasonality. Now I am trying to fit the 'seasonal part' of dataset http://stats.stackexchange.com/questions/14948/seasonal-data-forecasting-issues into ARIMA model and tried to forecast (with SPSS). The problem is, I get exactly same values in forecasts as that of actual values. So, MAPE is coming to be 0.000 Is this obvious to happen or am I doing something wrong? P.S. The data set is here: http://mihirsathe.com/mihir/STI/STI/drugs/index.html time-series spss arima share|improve this question edited Aug 30 '11 at 0:57 asked Aug 29 '11 at 16:48 mihsathe 1055 Have you predicted the last values of response variable for $t$ from 228 to 233? What are your predicted values? If you take in-sample accuracy statistics your residuals are quite large, note that random is multiplicative part as well as seasonal. So the additive residuals could be taken straightforward as actuals - fitted. So as compared to actuals your fit won't give you 0 in sample MAPE. Thus I guess you are doing something wrong. What are you actually doing? –Dmitrij Celov Aug 29 '11 at 19:20 Last predicted values are : 0.99 0.95 1.03 0.99 1.02 –mihsathe Aug 30 '11 at 0:55 @Dmitrij Celov Sir, I think ther
Locked 1 message Marc Vinyes-2 Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: modifying a built in function from the stats package (fixing arima) (CONCLUSIONS) Thanks a lot to everybody that helped http://r.789695.n4.nabble.com/Re-modifying-a-built-in-function-from-the-stats-package-fixing-arima-CONCLUSIONS-td880715.html me out with this. Conclusions: (1) In order to edit arima in R: >fix(arima) or alternatively: >arima<-edit(arima) (2) This is not contained in the "Introduction to R" manual. (3) A "productive" fix of arima is attached (arma coefficients printed http://grokbase.com/t/r/r-help/123pzdtky5/r-how-to-make-this-time-series-data-stationary out and error catched so that it doesn't halt parent loops to search for candidate coefficients): Note 1: "productive" means I'm a beginner in R so there is probably a better way to print the error message and error in fill the output arguments (I only return NA in aic,var and sigma2). Note 2: Changing BFGS to NelderMead in "exitpoint 0" changes the coefficients for which arima can't fit a model but results in terms of aic and sigma2 also change significantly. By visual inspection I think that BFGS works better. function (x, order = c(0, 0, 0), seasonal = list(order = c(0, 0, 0), period = NA), xreg = NULL, include.mean = TRUE, transform.pars = error in arima TRUE, fixed = NULL, init = NULL, method = c("CSS-ML", "ML", "CSS"), n.cond, optim.control = list(), kappa = 1e+06) { "%+%" <- function(a, b) .Call(R_TSconv, a, b) upARIMA <- function(mod, phi, theta) { p <- length(phi) q <- length(theta) mod$phi <- phi mod$theta <- theta r <- max(p, q + 1) if (p > 0) mod$T[1:p, 1] <- phi if (r > 1) mod$Pn[1:r, 1:r] <- .Call(R_getQ0, phi, theta) else if (p > 0) mod$Pn[1, 1] <- 1/(1 - phi^2) else mod$Pn[1, 1] <- 1 mod$a[] <- 0 mod } arimaSS <- function(y, mod) { .Call(R_ARIMA_Like, y, mod$phi, mod$theta, mod$Delta, mod$a, mod$P, mod$Pn, as.integer(0), TRUE) } armafn <- function(p, trans) { par <- coef par[mask] <- p trarma <- .Call(R_ARIMA_transPars, par, arma, trans) Z <- upARIMA(mod, trarma[[1]], trarma[[2]
autofit function from "itsmr" packagek<-read.table(file.choose())ar(k$V1)Call:ar(x = k$V1)Order selected 0 sigma^2 estimated as 0.2499autofit(k$V1)Error in arima(x, c(p, 0, q)) : non-stationary AR part from CSSwhat is this CSS ?& hot to make it stationary--View this message in context: http://r.789695.n4.nabble.com/how-to-make-this-time-series-data-stationary-tp4494907p4494907.htmlSent from the R help mailing list archive at Nabble.com. reply Tweet Search Discussions Search All Groups r-help 1 response Oldest Nested R. Michael Weylandt Please (!) go read a book on basic time series analysis instead of just posting a question each time you see a new word. Michael R. Michael Weylandt at Mar 22, 2012 at 1:13 pm ⇧ Please (!) go read a book on basic time series analysis instead ofjust posting a question each time you see a new word.MichaelOn Thu, Mar 22, 2012 at 4:56 AM, sagarnikam123 wrote:i have below file as time series datahttp://r.789695.n4.nabble.com/file/n4494907/1A2X_B_phi_psi_pot_r_k.txt1A2X_B_phi_psi_pot_r_k.txti used autofit function from "itsmr" packagek<-read.table(file.choose())ar(k$V1)Call:ar(x = k$V1)Order selected 0 ?sigma^2 estimated as ?0.2499autofit(k$V1)Error in arima(x, c(p, 0, q)) : non-stationary AR part from CSSwhat is this CSS ?& hot to make it stationary--View this message in context: http://r.789695.n4.nabble.com/how-to-make-this-time-series-data-stationary-tp4494907p4494907.htmlSent from the R help mailing list archive at Nabble.com.______________________________________________R-help at r-project.org mailing listhttps://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code. reply | permalink Related Discussions [R] coding problems [R] panel data unit root tests [R] Unit root [R] The null hypothesis in kpss test (kpss.test()) [R] Whittle estimation for ARMA models [R] How to analyse and model 2 time series, when one series needs to be differenced? [R] stationary "terminology" time series question [R] how to simulate a time series [R] cointegrating regression [R] Block length for Bivariate Stationary Bootstrap for Inference for Correlation Discussion Navigation viewthread | post Discussion Overview groupr-help @ Notice: Undefined variable: pl_domain_short in /home/whirl/sites/grokbase/root/www/public_html__www/cc/flow/tpc.main.php on line 1605 categoriesr postedMar 22, '12 at 8:56a activeMar 22, '12 at 1:13p posts2 user