similar to: Strange behaviour of `dlm` package

Hi All, I have a problem trying to forecast using the dlm package, can anyone offer any advise? I setup my problem as follows, (following the manual as much as possible) data for example to run code CostUSD <- c(27.24031,32.97051, 38.72474, 22.78394, 28.58938, 49.85973, 42.93949, 35.92468) library(dlm) buildFun <- function(x) { dlmModPoly(1, dV = exp(x[1]), dW = exp(x[2])) } fit <-

Dear R users, I am trying to use the dlm package, and in particular the dlmBSample function. For some reason that I am not able to understand, this function does not work properly and the plot of the result does not make sense, while dlmFilter works perfectly. I think that my_mod is correct, because the output of my_dlmFilter$mod is fine. Where is my mistake or my misunderstanding? This is the

(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 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 <-

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 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 having problems with forward prediction using the output of the Basic Structural Model from StructTS. The following snippet illustrates the problem: t_end <- 139 nahead <- 20 data(AirPassengers) ap <- log10(AirPassengers)-2 fit <- StructTS(ts(ap[1:t_end], freq=12), type="BSM") p <- stats:::predict.StructTS(fit, n.ahead=nahead) plot(1:t_end, ap[1:t_end],

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

To Whom It May Concern, I estimated a model with 6 states (3 time-varying Regression parameters and 3 quarterly seasonality trends). The model is saved in the object titled "mod." Following the example in the documentation and using the commands below, I am attempting to use the function "dlmSvd2var" to implement SVD and calculate the 90% confidence errors for each

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

This question pertains to setting up a model in the package "dlm" (dynamic linear models, http://cran.r-project.org/web/packages/dlm/index.html I have read both the vignette and?"An R Package for Dynamic Linear Models" (http://www.jstatsoft.org/v36/i12/paper), both of which are very helpful. There is also some discussion at

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