search for: tsdata

..., 22489054, 23442393, 24170736, 25308044, 25021560, 25724331, 25990336, 26602038, 27292250, 28891954, 29659700, 31533579, 32513666, 33628559, 34494451)) plot(hotann, ylab="Annual hotel bookings") # Analysis with log data tsdata <- log(hotann) buildfun <- function (x) { dlmModPoly(order = 2, dV = exp(x[1]), dW = c(0,exp(x[2]))) } fit <- dlmMLE(y=tsdata, parm=c(0,0), build=buildfun) # Warning: a numerically singular 'V' has been slightly perturbed to make it nonsingular fit$conv dlmTsdata <- buildfu...

....9850, 105.9697, 105.9604, 105.9509, 105.9430, 105.9357, 105.9314, 105.9333, 105.9420, 105.9640, 105.9994, 106.0290, 106.0855, 106.1265, 106.1197, 106.1245, 106.1893, 106.2118, 106.1503, 106.0883, 106.0511, 106.0194, 106.0221) # Set TSdata object arma.fit.TSdata <- TSdata(input = arma.fit.input, output = arma.fit.output) # Fit the model arma.model.without.trend <- estVARXls(arma.fit.TSdata, max.lag=1, trend=F) arma.model.with.trend <- estVARXls(arma.fit.TSdata, max.lag=1, trend=T) # Apply the model for the test period a...

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 <- TSdata(input=radar, output=rain) input data: Error: evaluation nested too deeply: infinite recursion / options(expressions=)?...

...5 0.025 0.01 0.119 0.146 0.176 0.216 ---- Lag truncation parameter: 1 CONCLUSION: Reject Ho at 0.05 sig level - Level Stationary Fail to reject Ho at 0.05 sig level - Trend Stationary > kpss.test(df,null = c("Trend")) KPSS Test for Trend Stationarity data: tsdata[, 6] KPSS Trend = 0.1298, Truncation lag parameter = 1, p-value = 0.07999 CONCLUSION: Fail to reject Ho - Trend Stationary as p-value < sig. level (0.05) > kpss.test(df,null = c("Level")) KPSS Test for Level Stationarity data: tsdata[, 6] KPSS Level = 1.0891,...

...sing 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 <- ts((df),frequency = 12, start = c(1999, 1)) The resulting time series is as follows: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1999 2 15 15 14 14 14 12 13 16 2 14 5 2000 6 8 10 17 11 9 18 11 1 4 7 3 I am unable to understand why the va...

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)) jpeg(filename="e:/tsmap.gif",width = 480, height = 480, units = "px", pointsize = 12, quality = 75, bg = "white", res = NA, restoreConsole = TRUE)...

...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: num 53.4 53.7 53.5 53.2 53.3 ... It shouldn't be difficult. Example: tsdata <- data.frame(year=c(2000,2002,2003), x=c(1,2,3),y=c(10,11,12)) xy.ts <- as.ts(tsdata) library(zoo) as.zoo(xy.ts) Berend Hasselman > $ wlth: num 60.3 60.5 60.2 60.1 60.7 ... > -- > Yadawananda Neog > Research Scholar > Department of Economics > Banaras Hindu Universit...

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 l.ARMA(setArrays(Shape, coefficients = coefficients), data, result = "like", : NA/NaN/Inf in foreign function call (arg 1...

...masked from package:stats : acf simulate > library("dse2") > > fileName <- system.file("otherdata", "eg1.dat", package="dse1") > eg1.DSE.data <- t(matrix(scan(fileName),5, 364))[, 2:5] Read 1820 items > > eg1.DSE.data <- TSdata(input= eg1.DSE.data[,1,drop = F], output= + eg1.DSE.data[, 2:4, drop = F]) > > eg1.DSE.data # this is the troublemaking command input data: Error: evaluation nested too deeply: infinite recursion / options(expressions=)? > #################################### # Second Example rary("...

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

...tember 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 betas<-read.table("C:\\Users\\Manta\\Desktop\\betas.txt",header=T,dec=",") BETA<-ts(betas,start=(1984),frequency=12) BETAS<-TSdata(output=BETA) VAR1<-estVARXls(window(BETAS,end=c(2003,12)),max.lag=1) pr<-forecast(VAR1,horizon=1) pr3<-forecast(VAR1,horizon=3) pr12<-forecast(VAR1,horizon=12) and the model is estimated correctly (same estimates as found using other softwares) Then the tricky part: I want to estimate...

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