search for: forecaste

I have a large Excel file with SKU numbers (stock keeping units) and forecasts which can be mimicked with the following: Period <- c(1, 2, 3, 1, 2, 3, 4, 1, 2) SKU <- c("A1","A1","A1","X4","X4","X4","X4","K2","K2") Forecast <- c(99, 103, 128, 63, 69, 72, 75, 207, 201) PeriodSKUForecast <-

I have a data frame that reads client ID date transcations 323232 11/1/2010 22 323232 11/2/2010 0 323232 11/3/2010 missing 121212 11/10/2010 32 121212 11/11/2010 15 ................................. I want to order the rows by client ID and date and using a black-box forecasting method create the data fcst(client,date of forecast, date for which forecast applies). Assume that I

Good day all, I'm trying to plot a continuous line plot, which is followed by two forecast points eg. one forecast point is 12 months out, and another 24 months out from the last date of the line plot. In my attempts so far, the second plot (the forecast points) is scaled against a new axis scale, thus the two plots are not directly comparable (I need the forecast points to be scaled

hi: I am trying to fit prediction intervals for an arima object. My search led me to the link: http://finzi.psych.upenn.edu/R/library/forecast/html/forecast.Arima.html which has the function "forecast", as I wanted. However, when I try to run it in R, I get the message: Error in plot(forecast(fit)) : could not find function "forecast" Even the example provided on the page

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

v1.0 of the forecasting bundle of packages is now on CRAN and will propagate to mirrors shortly. The forecasting bundle of R packages provides new forecasting methods, and graphical tools for displaying and analysing forecasts. It comprises the following packages: * forecast: Functions and methods for forecasting. * fma: All data sets from Makridakis, Wheelwright and Hyndman

Hi all, We followed some books and sample codes and did some EMM estimation, only to find it won't be able to generate forecast. This is because in the stochastic volatility models we are estimating, the volatilities are latent variables, and we want to forecast 1-step ahead or h-step ahead volatilities. So it is nice to have the system estimated, but we couldn't get it to forecast at

Hello, does anyone know of any R routines capable of whats called Hierarchical Forecasting, reconciling the different hierarchies. Example: A top down forecast where the corporate forecast is created and then all the regions within the corporate entity are also forecasted, with the constraint they sum to the corporate forecast.

Hello, I am using forecast() in the forecast package to predict future values of an ARIMA model fit to a time series. I have read most of the documentation for the forecast package, but I can't figure out how to obtain the forecast variance for the predicted values. I tried using the argument "se.fit=TRUE," hoping this would work since forecast() calls predict(). Is there an easy

hi all. i'm busy with some time series data, starting from an earlier period until the current day. i have created a time series forecast taking into account the entire data from the earlier date up until 2007, using the "forecast" package for R. i am comparing this forecasted data to the actual/ observed data (which starts from the earlier date up until the current day). my question is, how do i extract the estimated data from the forecast as actual values so that i can do further analysis with it? thanking you all in advance -- View this message in context: http://...

There appears to be a problem with name matching in list assignment: Version 0.63.0 (November 14, 1998) ... > r <- list() > r$forecast.cov.trend <- 1:12 > r$forecast.cov.zero <- 1:12 > r$forecast.cov <- 1:2 > length(r$forecast.cov) [1] 0 #should be 2 > But note that this works correctly: > r <- list() >

Couldn't find this in the archives. I'm fitting a series of historical weather-related data, but would like to use the latest values to forecast. So let's say that I'm using 1970-2000 to fit a model (using fourier terms and arima/auto.arima), but now would like to use the last X values to predict tomorrow's weather. I'm at a loss. All the functions I've come across

...Q4 8 $6,509,331 2009 Q1 9 $6,271,951 2009 Q2 10 $6,657,190 2009 Q3 11 $6,979,821 2009 Q4 12 $7,581,619 (Using the first report i want to generate the below report using R) is it possible? forecasted report for the year 2010 quarter Revenue Revenue Predictor 2007 Q1 $3,856,799 $4,032,525 2007 Q2 $4,243,328 $4,032,525 2007 Q3 $4,930,369 $4,032,525 2007 Q4 $5,443,579 $4,032,525 2008 Q1 $5,164,8...

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 =

Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

I'm currently working with some time series data with the xts package, and would like to generate a forecast 12 periods into the future. There are limited observations, so I am unable to use an ARIMA model for the forecast. Here's the regression setup, after converting everything from zoo objects to vectors. hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE) lm.model <-

Hello everybody! I have an ARIMA model for a time series. This model was obtained through an auto.arima function. The resulting model is a ARIMA(2,1,4)(2,0,1)[12] with drift (my time series has monthly data). Then I perform a 12-step ahead forecast to the cited model... so far so good... but when I look the plot of my forecast I see that the result is really far from the behavior of my time

Hello everyone, I'm doing some benchmark comparing Arima [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

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

Dear all, I stumbled upon what appears to be a troublesome issue when sampling from an ARIMA model (from Rob Hyndman's excellent 'forecast' package) that contains a seasonal AR component. Here's how to reproduce the issue. (I'm using R 2.9.2 with forecast 2.19; see sessionInfo() below). First some data: > x <- c( 0.132475, 0.143119, 0.108104, 0.247291, 0.029510,