similar to: Question about arima.sim()

I have data as below.Please let me know how the ACF and Pacf used to determine the order od arima model. Is there any rules need to be followed to determine order.Please advise > turkey.price.ts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2001 1.58 1.75 1.63 1.45 1.56 2.07 1.81 1.74 1.54 1.45 0.57 1.15 2002 1.50 1.66 1.34 1.67 1.81 1.60 1.70 1.87 1.47 1.59 0.74 0.82

two ARIMA models, both have several bars signicant in ACF and PACF plots of their residuals, but when run Ljung Box tests, both don't show any significant correlations... however, one model has p-value that is larger than the other model, based on the p-values, can I say the model with larger p-values should be better than the model with smaller p-values? [[alternative HTML version

Dear all R users, I am really struggling to determine the most appropriate lag order of ARIMA model. My understanding is that, as for MA [q] model the auto correlation coeff vanishes after q lag, it says the MA order of a ARIMA model, and for a AR[p] model partial autocorrelation vanishes after p lags it helps to determine the AR lag. And most appropriate model choosed by this argument gives

Hi all, I am modelling a time series with missing data. *Q1)* However, I am not sure if I should use the next *graphics* to understand my data: *a)* ACF & PACF (original series) *b)* ACF & PACF (residuals) * * *Q2)* I am using *tsdiag*, so I obtain a graphic with 3 plots: stand. residuals vs time; acf for residuals; Ljung-Box for residuals (it is wrong for residuals). I know that using

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Dear R-helpers, I have a time series analysis problem in R: I want to analyse the output of my simulation model which is proportional cover of shrubs in a savanna plot for each of 500 successive years. I have run the model (which includes stochasticity, especially in the initial conditions) 17 times generating 17 time series of shrub cover. I am interested in a possible periodicity of shrub

Hey guys, im sort of a beginner with R, but here's what i need to do. i need to perform a time series analysis on a set of financial data that i've been given. im trying to look at the ACF and PACF and fit it to a particular model (i think its the ARIMA model because i've read that financial data resembles the random walk, where the ARIMA model fits). is this correct? if my data is

Hey guys, im sort of a beginner with R, but here's what i need to do. i need to perform a time series analysis on a set of financial data that i've been given. im trying to look at the ACF and PACF and fit it to a particular model (i think its the ARIMA model because i've read that financial data resembles the random walk, where the ARIMA model fits). is this correct? if my data is

Good afternoon! I'm trying to model a ts but unfortunately i'.m very new to this kind of modeling so i 'll be very grateful if you have an advice. This was my syntax:

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

Hello Michael, see as an online resource: http://www.statsoft.com/textbook/sttimser.html or get hold on a time series analysis textbook, like one of the monographies written by Hamilton; Luetkepohl; Brockwell & Davis; Harvey or Box & Jenkins, to name but a few. In a nutshell, PACF 'eliminates' intermediate autocorrelations compared to ACF, e.g. an AR(1) process will ordinarily

Dear All, I have a model to predict time series data for example: data(LakeHuron) Lake.fit <- arima(LakeHuron,order=c(1,0,1)) then the function predict() can be used for predicting future data with the model: LakeH.pred <- predict(Lake.fit,n.ahead=5) I can see the result LakeH.pred$pred and LakeH.pred$se but I did not see residual in predict function. If I have a model: [\ Z_t =

My question is: is there a way for Rtangle() to *not* print re-used code chunks? It'd be easy enough to brew up a perl script to do just this, but if methods exist already, I'd rather use them. My reading of the help pages and FAQs has missed something, if it's there. Background: I have course notes on R, written using Sweave. I want to provide the R code separately so the

Good afternoon! I'm trying to model a time series on the following data, which represent a monthly consumption of juices: >x<-scan() 1: 2859 3613 3930 5193 4523 3226 4280 3436 3235 3379 3517 6022 13: 4465 4604 5441 6575 6092 6607 6390 6150 6488 5912 6228 10196 25: 7612 7270 8617 9535 8449 8520 9148 8077 7824 7991 7660 12130 37: 9135 9512 9631 12642

please i've a big problem. i've to do a econometric-quantitative methods assignment about the canadian lynx, the problem is that i really i don't know how to use r and how to apply all the steps. I begun the time plot, ACF and PACF but i'm not able to decide what is the correct model of ARIMA, Holt-winter, ecc to forecast the next 20 years of canadian lynx's cyle... if someone

hi, in 1.9.1, the return value from ARMAacf(pacf=TRUE) is not named by lags, contrary to ?ARMAacf. the simple fix is to move names(Acf) <- down after if(pacf), with an appropriate starting lag as pacf=TRUE appears to start at lag 1 (whereas pacf=FALSE starts at lag 0). for consistency, one could argue to append 1 for lag 0 for pacf=TRUE (or start pacf=F at lag 1). however, given the

Hi! I want to plot three graphs (residuals, ACF and PACF of a model). Ideally I would use a c(2,2) disposition where the residuals plot would start at position 1,1 and span to position 1,2. Then I would plot the ACF in position 2,1 and the PACF in position 2,2. Maybe is clearer like this: -------------------------- | | | residuals | |

Hi R-sters, Just wondering what I might be doing wrong. I'm trying to fit a multiple linear regression model, and being ever mindful about the possibilities of autocorrelation in the errors (it's a time series), the errors appear to follow an AR1 process (ar(ts(glsfit$residuals)) selected order 1). So, when I go back and try to do the simultaneous regression and error fit with gls,

Hi helpers, can you help me in plotting acf and pacf functions in R. I am using the code acf(variable name) but it is not working. Expecting your reply. Thanks -- View this message in context: http://n4.nabble.com/ACF-and-PACF-tp1477149p1477149.html Sent from the R help mailing list archive at Nabble.com.

R: Is there an easy way to get the acf and pacf for an irregular times series? That is, the acf and pacf with lag lengths that are in units of time, not observation number. Thanks, Jason Higbee Research Associate Federal Reserve Bank of St. Louis The views expressed in this email are the author's and not necessarily those of the Federal Reserve Bank of St. Louis or the Federal Reserve