similar to: business forecasting service

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

Sender: r-help-bounces at r-project.org On-Behalf-Of: comtech.usa at gmail.com Subject: Re: [R] [R-SIG-Finance] forecasting earnings, sales and gross margin of a company... Message-Id: <b1f16d9d0810231239k506d582i7ecb908b84bc1642 at mail.gmail.com> Recipient: ngottlieb at marinercapital.com -------------------------------------------------------- This information is being sent at the

Hi all, I am playing with some companies' balance sheets and income statements and want to apply what I've just learned from Stats class to see if I can forecast the companies earnings, sales and gross margin in the short term (3rd and 4th Quarter), mid-term (2009) and long term (2011, etc. ) I pulled up some data from companies' financial statements over the past a few years. The

Chan Chung Hang Christopher <christopher.chan at bradbury.edu.hk> wrote: >> Ah, well, if you want to keep the landlines, then yeah, I guess asterisk is the way to go. If your goal is to replace keyline systems, then asterisk definitely has that kind of support which, it appears, even Cisco's solution does not (from the mouth of Datacraft Asia personnel selling the school

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

R gurus, I've exhausted my search of online help. This is my last resort. We're running R-2.1.1. I've been told by one of the R users here that we cannot upgrade to the lastest version because of some python rpy wrapper dependency that hasn't caught up to the latest version of R. The software is running on Solaris 10 x86. Gcc version is 3.4.1. R is installed in

Hi all, Does anyone have the knowledge to help me identify a package capable of forecasting a MULTIPLE regression model? i have a model with one one dependant variable and 4 independant variables. i would like to forecast confidence intervals for a few steps ahead...(DENSITY forecasting). PS i can forecast a univariate vector, with package 'forecast', however, i want one that can do

predict.warn() -- a function to display factor levels in new data for linear model prediction that do not exist in the estimating data. Date: 2005-8-24 From: John C. Nash (with thanks to Uwe Ligges for suggestions) nashjc at uottawa.ca Motivation: In computing predictions from a linear model using factors, it is possible to introduce new factor levels. This was encountered on a practical

I appear to have hit one of the "drop" issues raised in some discussions a couple of years ago by Frank Harrell. They don't seem to have been fixed, and I'm under some pressure to get a quick solution for a forecasting task I'm doing. I have been modelling some retail sales data, and the days just after Thanksgiving (US version!) are important. So I created some dummy

I use Asterisk as a wakeup service for home and regularly dial for weather forecast after waking up. I'd like to be able to setup a recurring wakeup call M-F and have it play the weather forecast rather than moh. I'm using AAH that has had all components recently upgraded. Has anyone done this sort of thing? Thanks, David -------------- next part -------------- An HTML attachment was

Hello, I would like to write a program that compute the principal components of a set of data and then 1. Run the dependent variable against the principal components (lagged value) 2. Do prediction , following Stock and Watson (1999) "Forecasting Inflation". All data are time series. Now I can run the program using single factor (first principal component), but I

Dear all, I am having difficulties using the seemingly-simple function lag. I have a dataframe with several weather variables (maxitemp, windspeed, rainfall etc), and the response variable (admissions). The dataset is fairly large (1530 observations). I simply want to model the response against a lag of a couple of the explanatory variables, say maxitemp and rainfall. I would like to look at

Hi, I tried to use the Croston function from the forecasting package 1.24<http://robjhyndman.com/software/forecasting> with the code below, but I get in return this message "*Error in decompose(ts(x[1L:wind], start = start(x), frequency = f), seasonal) : time series has no or less than 2 periods*". histValues

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.

Instead of Tsp = c(2016, 2018, 12) try Tsp = c(2016, 2018.25, 12) Hence, you can specify the object as structure(c(5973156.76, 5159011.2, 6695766.64, 6365359, 6495218.53, 7226302.39, 6835272.7, 7383501.57, 6962748.19, 7623278.72, 7274994.33, 7919421.8, 7360740.81, 7436693.35, 8545765.55, 7337269.76, 8180585.44, 8376635.05, 7758261.24, 10374641.22, 8000314.11, 9114958.9, 9805149.15,

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

Hello everybody! I'm having this problem with the auto.arima function that i've not been able to solve. I use this function on time series that contains NA values, but every time that the resulting model contains drift I can't perform a forecasting (using forecast.Arima function). The printed error (when I try to forecast the resulting model) claims a dimension mismatch

Dear all, I just have a short question regarding the forecasting of ARIMA models with external regressors. I tried to program a ARX(1) model arx.mod <- arima(reihe.lern, order = c(1, 0, 0), seasonal = list(order = c(0, 0, 0), period = 52), xreg = lern.design, include.mean = TRUE) for which I need to estimate the next (105th) value. Xreg=lern.design is - at this time - 104 rows long. I

Dear R-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar