similar to: VAR forecasts and out-of-sample prediction

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

Hi, I work in a kitchen production factory and I try to predict sales of kitchen for an horizon of 12 weeks, and I have to turn into account promotions. My sales are express in number of command, my promotion are express with dummy variables. (1 if promotion, 0 else). The first problem is that my time serie contain a trend and a seasonality. (but the serie is stationary) I use the VAR function

Hello, Using the dse package I have estimated a VAR model using estVARXls(). I can perform forecasts using forecast() with no problems, but when I try to use simulate() with the same model, I get the following error: Error in diag(Cov, p) : 'nrow' or 'ncol' cannot be specified when 'x' is a matrix Can anyone shed some light on the meaning of this error? How can I

Dear R-help! I have encountered strange behaviour (that is, far-off filtering, smoothing and forecast distributions under certain conditions) in the `dlm` package by Giovanni Petris. Here is an example: I use the annual hotel bookings time series data, which I model using a second order polinomial DLM. First I perform the analysis with the data in logarithmic form and everything seems to be

dear All, I have the following problem: I need to calculate an h-step ahead forecast from a var model (estimated with a dse1 method estVARXls), which in turn will be used as an input for another model as conditioning data, so I need it as a simple, numeric matrix. No exogenous input is used. However, the standard forecast method produces a 1-element list that includes a forecast matrix, yet I

Hi All, Currently I'm using DSE package for Kalman Filtering. I have a dataset of one dependent variable and seven other independent variables. I'm confused at one point. How to declare the input-output series using TSdata command. Because the given example at page 37 showing some error. rain <- matrix(rnorm(86*17), 86,17) radar <- matrix(rnorm(86*5), 86,5) mydata <-

Hi, Am I interpreting the results properly? Are my conclusions correct? > KPSS.test(df) ---- ---- KPSS test ---- ---- Null hypotheses: Level stationarity and stationarity around a linear trend. Alternative hypothesis: Unit root. ---- Statistic for the null hypothesis of level stationarity: 1.089 Critical values: 0.10 0.05 0.025 0.01 0.347 0.463

I apply isoMDS to my data, but the result turns out to be bad as the stress value stays around 31! Yeah, 31 ,not 3.1... I don't know if I ignore something before recall isoMDS. My code as follow: m <- read.table("e:/tsdata.txt",header=T,sep=",") article_number <- ts(m, start = 2004,end=2008, frequency = 1 ,names=colnames(m))

> On 15 Sep 2017, at 11:38, yadav neog <yadavneog at gmail.com> wrote: > > hello to all. I am working on macroeconomic data series of India, which in > a yearly basis. I am unable to convert my data frame into time series. > kindly help me. > also using zoo and xts packages. but they take only monthly observations. > > 'data.frame': 30 obs. of 4 variables:

Hi, I am trying to convert a dataset (dataframe) into time series object using ts function in stats package. My dataset is as follows: >df [1] 11.08 7.08 7.08 6.08 6.08 6.08 23.08 32.08 8.08 11.08 6.08 13.08 13.83 16.83 19.83 8.83 20.83 17.83 [19] 9.83 20.83 10.83 12.83 15.83 11.83 I converted this into time series object as follows >tsdata <-

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

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 It's my understanding that as of 3.0 rsync supports OS X ACLs and extended attributes, and that it should be possible to backup a tree from an OS X system that contains files and folders with ACLs and extended attributes to a Linux host filesystem that has both "user_xattrs" and "acl" enabled on the destination file system.

Hi, I tried to run the VARMA model in the dse package. I specified a model: > arma A(L) = 1+0.244L1 0+0.05L1 0-0.325L1 1-0.234L1 B(L) = 1-0.277L1 0+0.211L1 0-0.206L1 1+0.238L1 and have a TSdata object: > dfdata output data: Series 1 Series 2 1 "difex2" "difem2" but I get this warning message: > estMaxLik(arma, dfdata) Error in

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 R-Community, so far I dealt with univariate processes and used the function "arima" to estimate an ARMA(1,1)-model. For multivariate processes there are the functions "estVARXar" and "estVARXls" from package "DSE". But how can I estimate an VARMA(1,1)-model, or even better determine the orders and estimate the parameters? Much thanks in advance, Hagen

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

All: How do I obtain one step ahead out-of-sample forecasts from a model using "dyn" or "dynlm" ? Thanks! Best, John [[alternative HTML version deleted]]

predict.warn() -- a function to display factor levels in new data for linear model prediction that do not exist in the estimating data. Date: 2005-8-24 From: John C. Nash (with thanks to Uwe Ligges for suggestions) nashjc at uottawa.ca Motivation: In computing predictions from a linear model using factors, it is possible to introduce new factor levels. This was encountered on a practical

I am having problems with the package dse. I just installed R 2.2.1 and reinstalled all packages. I am running Windows XP Pro with all updates. Below there are two examples of error messages generated when trying to execute some simple programs. The code was taken directly from the package documentation. Any help on this will be greatly appreciated. Merry Christmas Fernando

Hello: I would like to generate rolling, multiperiod forecasts from an estimated ARIMA model, but the function predict.Arima seems only to generate forecasts from the last observation in the data set. To implement this, I was looking for an argument like 'newdata=' in predict.lm. I can write some code that does this for my particular problem, but might there exist a