similar to: question on adding confidence intervals

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 <-

I have two variables. A <- rep(c(1:9), 2) B <- rep(c(2:10),2) -- Renuka Sane http://www.nyx.net/~rsane [[alternative HTML version deleted]]

Question on graphs: The default case for drawing a graph in R, is where a little space is left on the x and y axis before the first tick i.e. even if I say xlim=c(0,1) -- there will be some space between the edge of the x-axis and where 0 is placed. If I want 0 on the edge, how do I do it in R? Area under the curve: I have a 45 degree line and a curve above or below it. Is there a way in R

Hi, I am would like to ask few questions. I am trying to forecast hourly electricity prices by 24 hours ahead. I have hourly data starting from 2015*12*18 to 2017-10-24 and I have defined the data as time series as written in the code below. Then I am trying do neural network with 23 non-seasonal dummies and 1 seasonal dummy. But I don?t know whether training set is enough.( Guess it is 50

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

Good afternoon! I'm trying to model a time series on the following data, which represent a monthly consumption of juices: >x<-scan() 1: 2859 3613 3930 5193 4523 3226 4280 3436 3235 3379 3517 6022 13: 4465 4604 5441 6575 6092 6607 6390 6150 6488 5912 6228 10196 25: 7612 7270 8617 9535 8449 8520 9148 8077 7824 7991 7660 12130 37: 9135 9512 9631 12642

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

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

Dear members, I'm fitting linear model using "lm" which has numerous auto-regressive terms as well as other explanatory variables. In order to calculate prediction intervals, i've used a for-loop as the auto-regressive parameters need to be updated each time so that a new forecast and corresponding prediction interval can be calculated. I'm fitting a number of these models

Hello, I?ve got a statistical problem that I hope you can help me with. It doesn?t have to do directly with R, so if there?s another forum which would suit better, please tell me! Now here?s the problem: I want to derive confidence intervals for a variable X, which is - given the descriptive statistics - obviously negatively skewed and leptokurtic (i.e. peaked). My aim is to make a statement

Good morning,??Please help me to code this code in R. I working in the multivariate time series data, know my objective is that to one year forecast of the hourly time series data, using first five as a training set and the remaining one year as validation. For this??I transform the the data into functional data through Fourier basis functional, apply functional principle components as dimensional

Hi all, Forecasting from an arima model is easy with predict. But I can't manage to backcast : invent data from the model before the begining of the sample. The theory is easy : take your parameters, reverse your data, forecast, and then reverse the forecast I've tried to adapt the predict function to do that (i'm not sure that the statistical procedure is fine (with the residuals),

1. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec; datalines; 2008 12 13 12 14 13 12 11 15 10 12 12 12 2009 12 13 12 14 13 12 11 15 10 12 12 12 ; run; I converted the above data into the below format to use it in R as it was giving error: asking

Good morning dear administrators, Please help me to code this code in R. I working in the multivariate time series data, know my objective is that to one year forecast of the hourly time series data, using first five as a training set and the remaining one year as validation. For this??I transform the the data into functional data through Fourier basis functional, apply functional principle

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

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

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

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

Good evening! I have a question regarding forecast package and time series analysis. My syntax: x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244, 258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302, 322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287, 267, 267, 288, 304, 273, 264, 254, 263, 265, 278) library(forecast) arima(x, order=c(1,1,2),

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