similar to: Fitting Arima Models and Forecasting Using Daily Historical Data

I guess this is more r-devel than r-help. Note, I am just the messenger - I have no idea what the user is trying to model here. arima() crashes R (segfault) with Linux R-2.7.0, Solaris R-2.6.0: *** caught segfault *** address 42400000, cause 'memory not mapped' Traceback: 1: .Call(R_getQ0, phi, theta) 2: makeARIMA(trarma[[1]], trarma[[2]], Delta, kappa) 3: arima(x, c(1, 0, 1), c(1,

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

in ?arima (R-1.9.1), the return value component 'convergence' should be 'code'? (it's a pity there is no reliable way to check return value documentation consistency with the code, or is there?) h. ---------------------------------- Hiroyuki Kawakatsu School of Management and Economics 25 University Square Queen's University, Belfast Belfast BT7 1NN Northern Ireland

in ?arima (R-1.9.1), the return value component 'convergence' should be 'code'? (it's a pity there is no reliable way to check return value documentation consistency with the code, or is there?) h. ---------------------------------- Hiroyuki Kawakatsu School of Management and Economics 25 University Square Queen's University, Belfast Belfast BT7 1NN Northern Ireland

Hi R-Experts, Currently I'm using an univariate time series in which I'm going to apply KalmanLike(),KalmanForecast (),KalmanSmooth(), KalmanRun(). For I use it before makeARIMA () but I don't understand and i don't know to include the seasonal coefficients. Can anyone help me citing a suitable example? Thanks in advance. ------------------------------------------

Thanks a lot to everybody that helped 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 out and error catched so that it doesn't halt parent loops to search for

Dear R friends, I am currently working with time series data, and I have a table(as data frame) that has looks like this (TransitDate are in format = "%e-%B-%Y") : TransitDate Transits CargoTons 1985-04-01 100 2500 1985-05-01 135 4500 1985-06-01 120 1750 1985-07-01 100 3750 1985-08-01 200

Hi Paul, #First I set up some sample data since I don't have a copy of your data dtOrig <- as.Date( c("1985-04-01","1985-07-01","1985-12-01","1986-04-01")) dfOrig <- data.frame( TransitDate=dtOrig, Transits=c(100,100,500,325), CargoTons=c(1000,1080,3785,4200) ) #Generate the complete set of dates as a data frame dfDates<- data.frame(

Dear Eric, Hope you are doing great. I also tried the following: #First I created the complete date sequence TransitDateFrame <- data.frame(TransitDate=seq(as.Date(dataset1[1,1]), as.Date(dataset1[nrow(dataset1),1]), by = "month")) #Then I did the merging dataset1NEW <- merge(TransitDateFrame, dataset1, by="TransitDate", all.x=TRUE) Now it has, as expected the

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

1. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec; datalines; 2008 12 13 12 14 13 12 11 15 10 12 12 12 2009 12 13 12 14 13 12 11 15 10 12 12 12 ; run; I converted the above data into the below format to use it in R as it was giving error: asking

Dear friend, hope you are doing great, I have the following code: > myTseriesData <- ts(data[,2], start=c(2000,01), end=c(2017,9), frequency=12) > myTseriesModel <- auto.arima(myTseriesData, d=1, D=1) > myTseriesForecast <- forecast(myTseriesModel, h=12) > # I want to be able to format the dates generated by the forecast function is there a way to change the format of the

Hi Instead of attachments copy directly result of dput(TransitDateFrame) and dput(dataset1) to your email. Or, if your data have more than about 20 rows you could copy only part of it. dput(TransitDateFrame[,1:20]) dput(dataset1[,1:20]) Only with this approach we can evaluate your data in all aspects and provide correct answer. Cheers Petr > -----Original Message----- > From: R-help

Hi R, Seems like the maximum seasonal 'q' parameter for the ?arima is 350. Any way, where we can increase this? Since I am working on 3 year (q=252*3) and 5 year(q=252*5) returns, I may require this option. Thanks. > fit=arima(r,c(3,0,0),seasonal = list(order = c(0, 0, 500), period = NA));tsdiag(fit);fit$aic Error in makeARIMA(trarma[[1L]], trarma[[2L]], Delta, kappa) :

Are any seasonal adjustment programs, like Tramo/Seats, Census X12 ARIMA or Berliner Verfahren implemented in R? I am doing a simulation study and I don't know how to adjust the series in R. The possibility to access external the exe.files of the seasonal adjustment programs seems to be quite difficult. Can anyone help me? Thanks, Ingo

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,

Hi I'm trying to measure the effect of a policy intervention (Box and Tiao, 1975). This query has to do with the coding of the model rather than with the particulars of my dataset, so I'm not providing the actual dataset (or a simulated one) in this case, apart from some general description. The time series are of length n=34 (annual observations between 1977 and 2010). The policy

Thank you very much David. As a matter of fact, I solved it by doing the following: MyTimeSeriesObj <- ts(MyData, freq=365.25/7, start=decimal_date(mdy("01-04-2003"))) After doing that adjustment, my forecasts dates started from 2017 on. Cheers, Paul 2017-12-06 12:03 GMT-05:00 David Winsemius <dwinsemius at comcast.net>: > > > On Dec 6, 2017, at 5:07 AM, Paul

Dear R-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar

Hello everyone, I'm doing some benchmark comparing Arima [1] and SVR on time series data. I'm using an out-of-sample one-step-ahead prediction from Arima using the "fitted" method [2]. Do someone know how to have a two-steps-ahead forecast timeseries from Arima? Thanks, Matteo Bertini [1] http://robjhyndman.com/software/forecast [2] AirPassengers example on page 5