similar to: using forecast() in dse2 with an ARMA model having a trend component

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

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 <-

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 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 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

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

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: $ year: int 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 ... $ cnsm: num 174 175 175 172 173 ... $ incm:

Hi, I want to plot this matrix (I attach the data), it is suposed that each column is a different time series. If I do g<-read.table("dataADF.txt", header=F) and plot(g[,1],type="l") it plots the first column plot if I want in a unique graph each colums of dataA, all in one. How should I proceed?There is a direct pre-defined code? And If I wanted a plot by each

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

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u

Hola a todos Estoy tratando de analizaruna serie temporal con datos diarios de temperaturas (40 años). He creado un objeto zoo (con ayuda de la lista, gracias) sobre el que encuentro la regresión lineal. He probado también a crear un objeto ts a partir del zoo. El problema que encuentro es que nose puede aplicar la función stl para hallar la componente estacional y la tendencia. Rdice que la