similar to: rolling n-step predictions for ARIMA models

Dear R-users, I am facing a trivial problem when trying to parameterise the start date of a time series object. I am working with monthly data (104) performing n-steps-ahead (6) forecasts and using a fixed window size (36). At the end of calculations I have a list that contains 69 forecasts. I have no problems in fixing the window size by parametrization, e.g. k<- control variable in a for

Hi, Sorry for the confusion. I would like to estimate a model wherein the marginals of z with respect to w1 and w2 are smooth functions of x and y. I have data on z, x, y, w1 and w2. so E[dz/dw1] = f(x,y) and E[dz/dw2] = g(x,y) and I would like to estimate f(x,y) and g(x,y) I suppose I could try to fit something more general using projection pursuit, but the nature of the problem suggests

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

Dear all, below you find enclosed my message from January 9th and my program (attention: beginner). Thanks for both answers! a. However, as far as I know the cosine is not the same as the Pearson correlation (only in special cases). b. Reid Huntsinger's hint was very useful, however I had to transpose the matrix first, for I want to calculate the distance of the _rows_. Regards, Petra

Dear R users, I am putting together reading and resources lists for spatial statistics and spatial econometrics and am looking for some pointers from more experienced practitioners. In particular, I find two "camps" in spatial modelling, and am wondering which approach is better suitied to which situation. The first camp is along the lines of Venables and Ripley's Chapter 14

Good morning everyone, I have attached an Excel file that contains a macro from which I call and use R's auto.arima function to generate forecasts. The program runs perfectly and it gets me the results; however, those results are pretty unusual. I also tried using the auto.arima function directly in the R console and still get weird results. The results are shown in columns AB, AC and AD

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

hello, I have a question about making n step ahead forecasts in cases where test and validation sets are availiable. For instance, I would like to make one step ahead forecasts on the WWWusage data so I hold out the last 10 observations as the validation set and fit an ARIMA model on the first 90 observations. I then use a for loop to sequentially add 9 of the holdout observations to make 1

Dear users, I am struggling with this issue. I want to estimate a VAR(1) for three variables, say beta1 beta2 beta3, using monthly observations from January 1984 to September 2009. In-sample period January 1984 to December 2003, out-of-sample January 2004 to September 2009. This is what I have done at the moment

Hello again, A different but related question to my last one: Does anyone know if one can easily estimate state-space models using ML and the kalman filter using R? I would be especially interested in a relatively flexible function that would allow for estimation of hyperparameters, or could be made to do so. Thanks Michael J. Roberts Resource Economics Division, PMT USDA-ERS 202-654-5557

I'm using the arima function to estimate coefficients and also using predict.Arima to forecast. This works nicely and I can see that the results are the same as using SAS's proc arima. I can also take the coefficent estimates for a simple model like ARIMA(2,1,0) and manually compute the forecast. The results agree to 5 or 6 decimal places. I can do this for models with and without

Hi! In October I worked with a client-server (using winsock.dll) 16 bit aplication emulated in wine (Not using ODBC), it worked fine. This aplication needs to connect to a server using port ctsql 5557/tcp, this service is in my /etc/services. And wine made all realy fine (Version 20011108). With the release 20011226 (I think)started the problem. Something changed with winsock 16-bit. I've

Hi i have a tdm2400p and installed asterisk 1.4.11 with zaptel 1.4.5 im having an error message when in running asterisk with the tdm card in. here's the error from the console of asterisk: [Nov 18 10:30:44] ERROR[5557]: chan_zap.c:7489 mkintf: Unable to get span status: Inappropriate ioctl for device [Nov 18 10:30:44] ERROR[5557]: chan_zap.c:10466 build_channels: Unable to register channel

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

Dear R gurus, How do I add dummy variable zero/ one in the ARIMA model " both in the in-sample and outside the sample(forecasts)? Regards Peter South Africa Tell: +2712 422 7357 Please Note: This email and its contents are subject to our email legal notice which can be viewed at http://www.sars.gov.za/Email_Disclaimer.pdf [[alternative HTML version deleted]]

Looking at the arima.c code related to arima fitting I noticed that the code is mainly a merge of: - Gardner, G, Harvey, A. C. and Phillips, G. D. A. (1980) Algorithm AS154. An algorithm for exact maximum likelihood estimation of autoregressive-moving average models by means of Kalman filtering. Applied Statistics 29, 311–322. - Jones, R. H. (1980) Maximum likelihood fitting of ARMA models to

Hi, This is regarding the ARIMA model. I am having time series data of stock of 2000 values. Using the ARIMA model in R, I want the forcasted values for next 36 time points. However when I run this model in R, I am getting same value for all 36 time points. I have tried to fit the data with ARIMA model by changing the parameters p,d,q after looking at the errors and other criteria for

Hi all, First: I have a small line of code I'm applying to a variable which will be placed in a matrix table for latex output of accuracy measures: acc.aarima <- signif(accuracy(forecast(auto.arima(tix_ts, stepwise=FALSE), h=365)), digits=3). The time series referred to is univariate (daily counts from 12-10-2010 until 5-8-2010 (so not 2 full periods of data)), and I'm working on

Hello, I need to know how to import ARIMA coefficients. I already determined the coefficients of the model with other software, but now i need to do the forecast in R. For Example: I have a time series named x and i have fitted an ARIMA(1,0,1) (with other software) AR coef = -.172295 MA coef = .960043 (i know that this is not a good model, it's just an example) I try to

Colleagues I am a new to R but already love it. I have the following problem: I fitted arima model to my time series like this (please ignore modeling parameters as they are not important now): x = scan("C:/data.txt") x = ts(x, start=1, frequency=1) x.fit<-arima(x, order = c(1,0,0), seasonal = list(order=c(0,0,1))) Now I want to use this model for forecasting and backtesting (!).