search for: airpasseng

Hi, When I run this code a part of my x-axis and y-axis changes color. Can somebody tell me what is wrong? Also, is there a way to control the color of the average lines? monthplot(AirPassengers+500, ylim=c(min(AirPassengers), max(AirPassengers+500)), ylab="") par(new=T) monthplot(AirPassengers, col="blue", ylim=c(min(AirPassengers), max(AirPassengers+500)), ylab="") Greetings from Norfolk, Nuno Prista _________________________________________...

data(AirPassengers) brings AirPassengers into the workspace. How can I  idenfity what the structure of AirPassengers is? Is it a data.frame, a table etc. etc. [[alternative HTML version deleted]]

Hi, i want to construct a data set similar to "AirPassengers". Its attributes are following. > attributes(AirPassengers) $tsp [1] 1949.000 1960.917 12.000 $class [1] "ts" How Can I construct a data set similar to it having same class and attributes. Thanks -- Amar Kumar Nandan ?:nandan.amar at gmail.com http://aknandan.co.nr

Hola usuarios, Necesito detectar si existe o no un cambio de tendencia y si dicho cambio es significativo, para una serie temporal del tipo AirPassengers, en la que a partir de un determinado momento se ha hecho una campaña (supongamos que una promoción de vuelos). Para ello he pensado varios métodos: Usar la descomposición espectral de la muestra [decompose(AirPassengers)] y luego una Regresión Joinpoint para la serie de tendencia, pero descono...

Greetings: I'm using the time series decomposition routine "stl" from the package "stats". But how do I get the results into a vector to work with them? example: data(AirPassengers) m<-stl(AirPassengers,"per") print(m) This lists the output but can't figure out how to extract the individual series like seasonal, trend, irregular. Thanks, Bob --------------------------------- [[alternative HTML version deleted]]

Given: data(AirPassengers) I get a ts data structure AirPassengers in the workspace. How can I generate the code that can create that structure? That is, given an example of a data structure, is there a way to generate the code that can greate that structure? Alternatively, is there a reference that provides a list o...

Hello, I'm an R newbie. I've tried to search, but my search skills don't seem up to finding what I need. (Maybe I don't know the correct terms?) I need the standard errors and not the confidence intervals from an ARIMA fit. I can get fits: > coef(test) ar1 ma1 intercept time(TempVector) - 1900

Hello! I have such a problem... Estimated a model based on common data (you can find it in R library), and I wanted to plot the orginal values with the estimated one. Unfortunately I can only see the original values. Below is the code with data library: / library(forecast) data(AirPassengers) AP <- AirPassengers class(AP) start(AP) end(AP) frequency(AP) lgAP <- log(AP) t<-2:length(AP) APsL<-log(ts(lgAP, start=1949, frequency=12)) final<-(ts.intersect(lgAP,lgAPlag=lag(lgAP,-1), t=t, sin12 = sin(2*pi*t/12), cos12 = cos(2*pi*t/12),dframe=TRUE)) summary(AIRlm <-...

...> ## Example from Brockwell & Davis (1991, p.92) > ## answer (1 + 3*n)*2^(-n) > n <- 1:10; (1 + 3*n)*2^(-n) [1] 2.00000000 1.75000000 1.25000000 0.81250000 0.50000000 0.29687500 [7] 0.17187500 0.09765625 0.05468750 0.03027344 > > > > cleanEx(); ..nameEx <- "AirPassengers" > > ### * AirPassengers > > flush(stderr()); flush(stdout()) > > ### Name: AirPassengers > ### Title: Monthly Airline Passenger Numbers 1949-1960 > ### Aliases: AirPassengers > ### Keywords: datasets > > ### ** Examples > > ## Not run: ## These a...

Hello, I just ran the predict.StructTS function using the AirPassengers data and got a ridiculous result. Here's what I ended up with: http://24.210.155.111/PredictWhat!.pdf Who wrote this? Am I seriously supposed to think this function would accurately predict a time series? -AnalogKid

...y, mod$Z, mod$a, mod$P, mod$T, ??????????? mod$V, mod$h, mod$Pn, as.integer(nit), FALSE, fast = TRUE) ??????? 0.5 * sum(x)/length(y) ??? } #... #... above and remaining code is the same as the original code in StructTS #... } Running this version gives the following error: R> StructTS.v1(log(AirPassengers), type = "BSM") Error in KalmanLike2(y, Z, -1) : object 'R_KalmanLike' not found In the second version I modify the internal function 'KalmanLike2' using the function 'KalmanLike' instead of calling 'R_KalmanLike'. StructTS.v2 <- function (x, type =...

...ma [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

...apply(ls(),class)))); + dim(xyz)<-c(length(xyz)/2,2); + return(xyz) + } > xyz       [,1]              [,2]        [1,] "a"               "character"  [2,] "aa"              "character"  [3,] "abc"             "character"  [4,] "AirPassengers"   "character"  [5,] "allobj"          "character"  [6,] "allObjects"      "character"  [7,] "allObjects2"     "character"  [8,] "arrayFromAPL"    "character"  [9,] "classes"         "ch...

.... It will lead to an error when it is called inside a loop, probably the result of the byte compiler now enabled by default. When I turn it off, it works again, as in older versions of R. I tested on Win, Linux and Mac, and the problem occurs everywhere. Here is a reproducible example: z <- AirPassengers # this works window(z, start = c(1955, 1), end = c(1955, 1)) <- NA # but this does not for (i in 1) { window(z, start = c(1955, 1), end = c(1955, 1)) <- NA } # Error in stats::window(x = `*tmp*`, start = c(1955, 1), end = c(1955, : # object '*tmp*' not found # turning o...

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], xlim=c(0,t_end+nahead), type="l", ylim=c(0,1.2)) points((t_end+1):(t_end+nahead), p$pred[1:nahead], type=...

I have seen a couple of posts about this, but no solutions. The problem is fitting the Basic Structural Model (BSM) to the AirPassengers time series using StructTS. ?For this particular time series, if the length is reduced below 140 months, the BSM fits are bad.? The following illustrates the problem ap0 <- log10(AirPassengers) - 2ap<-ts(ap0[1:139], freq=12) ?# bad fits for length < 140(fit <- StructTS(ap, type= &quo...

I am using AUTO ARIMA for forecasting. But it is not detecting 'seasonality term' of its own for any data. Is there any other method by which we can detect seasonality and its frequency for any data? Is there any method through which seasonality and its frequency can be automatically detected from ACF plot? -- Siddharth Arun, 4th Year Undergraduate student Industrial Engineering and

...Squares Freedom Square F-value Between Years 236.0512 11 21.459200 2.681* Error 968.4973 121 8.004110 Here's me doing it manually with some sample data: library(x12) library(TSA) ### for cycle function data(AirPassengers) x12path <- "C:/WinX12/x12a/x12a.exe" x12out <- x12(AirPassengers,x12path=x12path, period=12,automdl=TRUE,transform="auto",outlier=c("AO","LS"),forecast_years= 0) si <- as.vector(x12out$d8) ### SI ratios -- i.e., final detrended series mo &lt...

...s) library(zoo) plot(zoo(AP.ts), ylim = c(0, 20)) AP.ts #is(AP.ts) #start(AP.ts);frequency(AP.ts);end(AP.ts) dput(AP.ts) AP.hw <- HoltWinters(AP.ts,seasonal="mult") plot(AP.hw) AP.predict <- predict(AP.hw,n.ahead=10) AP.predict ts.plot(AP.ts,AP.predict, lty=1:4) ### TESTE data(AirPassengers) aa <- AirPassengers aa is(aa) start(aa);frequency(aa);end(aa) Thank you guys!!!!! -- Vinicius Macedo Magalhães (21) 9584-1533 [[alternative HTML version deleted]]

Hi, I would like to create variables in R containing parameters of summary(*Forecast Results*). Using the following code: library(forecast) data <- AirPassengers xets <- ets(data, model="ZZZ", damped=NULL) xfor <- forecast(xets,h=12, level=c(80,95)) summary(xfor) the output is: Forecast method: ETS(M,A,M) Model Information: ETS(M,A,M) Call: ets(y = data, model = "ZZZ", damped = NULL) Smoothing parameters: alpha = 0.5...