similar to: ARIMA (seasonal) backcasting & interpolation

I create a zooreg object that runs from Jan-1-2002 0:00 to Jun-1-2005 0:00... regts.start = ISOdatetime(2002, 1, 1, hour=0, min=0, sec=0, tz="") regts.end = ISOdatetime(2005, 6, 1, hour=0, min=0, sec=0, tz="") regts.zoo <- zooreg( NA, regts.start, regts.end, deltat=3600 ) Upon inspection: > regts.zoo[1:3] 2002-01-01 00:00:00 2002-01-01 01:00:00 2002-01-01 02:00:00

The purpose of this email is to ask for pre-built procedures or techniques for smoothing and interpolating missing time series data. I've made some headway on my problem in my spare time. I started with an irregular time series with lots of missing data. It even had duplicated data. Thanks to zoo, I've cleaned that up -- now I have a regular time series with lots of NA's.

Hello, I problem is in the format of the date, my time series is like this: 2006070100 1244 61 62 2006070101 1221 60 60 2006070102 1214 60 60 2006070103 1194 59 59 2006070104 1182 58 58 2006070105 1178 58 58 2006070106 1176 58 58 2006070107 1173 58 58

Hello, I'm trying to develop a box plot of time series data to look at the range in the data values over the entire period of record. My data initially starts out as a list of hourly data, and then I've been using this code to make this data into the final ts array. # Read in the station list stn.list <- read.csv("/home/kbennett/fews/stnlist3", as.is=T, header=F) # Read in

Hello All, I trying to use the function auto.arima(....) from package forecast but I have a problem. My steps after I used the function auto.arima(...) I create the time series like this: >bbrass = scan("C:/Program Files/R/data PTIN/my_file.dat") >regts.start = ISOdatetime(2006, 7, 1, hour=0, min=0, sec=0, tz="GMT") #2006 07 01 00 >regts.end = ISOdatetime(2006, 7,

The following command crashes my Mac OS X version of R: (I'm running R on a PowerMac G5, with 1 GB of RAM and dual processors.) > arima.0 <- arima(w3.ts,order=c(1,0,0),seasonal=list(order=c (1,0,0),period=365)) -David Here is some background: w3.ts is hourly temperature data with about 20% NA's. It contains about 3.5 years of data. I am using period = 365, which makes

Hello, I using the fuction auto.arima() from package forecast to predict the values of p,d,q and P,D,Q. My problem is the execution time of this function, for example, a time series with 2323 values with seasonality to the week take over 8 hours to execute all the possibilities. I using a computer with Windows XP, a processor Intel Core2 Duo T7300 and 2Gb of RAM.

I'm trying to use the following command. arima (x, order = c(p,d,q), seasonal =list(order=c(P,D,Q), period=s) How can I write an estimated seasonal ARIMA model from the outputs. To be specifically, which sign to use? I know R uses a different signs from S plus. Is it correct that the model is: (1-ar1*B-ar2*B^2-...)(1-sar1*B^s-sar2*B^2s-....)(1-B)^d(1-B^s)^D

Hello, does anyone know about: a) simulating seasonal ARIMA models? arima out of package ts can fit it, but it does not look like it can simulates data from seasonal models b) fitting and simulating fractional seasonal ARIMA models? Hints will be appreciated, Henning -- Henning Rust Potsdam Institute for Climate Impact Research Dept. Integrated Systems Analysis Tel.: #49/331/288-2596

Hello, I am new to usin R - which is a great tool - and would like to know if R has a seasonal adjustment program for time series and/if it incorporates the Census Bureau's ARIMA x-12 seasonal adjustment program in any way? Thanks so much! Tony [[alternative HTML version deleted]]

Hello R People: When using the arima function with the seasonal option, are the seasonal options only good for monthly and quarterly data, please? Also, I believe that weekly and daily data are not appropriate for seasonal parm estimation via arima. Is that correct, please? Thanks, Sincerely, Laura Holt mailto: lauraholt_983 at hotmail.com download!

Hi all, I just couldn't find a R function which can fit multiple seasonal patters... i.e. in the following code: *arima(x = data, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), ... *** * there can be only one "period", am I right? What if the data seem to have three different seasonality cycles, 5, 12, 21? Thanks a lot! * [[alternative HTML version

To whom it may concern: I am trying to better understand the functionality of 'R' when making arima predictions to avoid any "Black Box" disadvantages. I'm fitting a seasonal arima model using the following command (having already loaded 'stat' package). arimaSeason <- arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12)) I can then generate

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,

Hello, I have daily wind speed data and need to fit seasonal ARIMA model, problem is that my period is 365. But when I use arima(...) function, with period 365, I?m getting error message: ?Error in makeARIMA(trarma[[1]], trarma[[2]], Delta, kappa) : maximum supported lag is 350?. Can someone help me with this problem? Thank you Sincerely yours, Laura Saltyte

hallo to all I've to calculate an arima model and I need only the first and 365 th parameter and also the sar1 and the intercept, so I'm traing with: arima(X,order=c(365,0,0),seasonal=list(order=c(1,0,0),..),fixed=c(NA,rep(0,363),NA,NA,NA),transform.pars=F) but the error answer is: Error in polyroot(z) : polynomial degree too high (49 max) also there are problems in allocating memory

Guys: Is it possible to simulate a seasonal ARIMA model in R? Which package can do this job? saji from Shanghai

Hello,guys: Recently, I am working on a seasonal ARIMA model. And I met some problem in the forecasting. Now I just want to know that How does R perform the predict procedure(the predict formula, the initial setting of errors,etc.)? I run the following commands and get the original code of the "predict" command, but I can't read it. Can anybody explain it to me? Thanks! saji from

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals),