forecasting

I recently updated R to 3.1.0. I tried to move my packages over to 3.1 inside the R.Framework (I'm running OSX Mavericks) and somehow made a mess of it, so did a complete uninstall of everything before a clean install. When reinstalling packages I had no issues until I hit the forecast package which yields the following error: package ‘forecast’ is available as a source package but not as a binary Warning in install.packages : package ‘forecast’ is not available (for R version 3.1.0) I actually had R 3.1.0 and both forecast and hts working together prior to the reinstall so I suspect the

I am trying to generate simulations from a seasonal arima model using the forecast package in R via the following command: simulate(model_temp) where model_temp is the result of applying the arima() function to my observed time series, and with which, incidentally, I specified the model to be an ARIMA(2,1,2)(0,1,2)[12] model. However, when I attempt this, I get the following error: Error in diffinv.vector(x, lag, differences, xi) : NA/NaN/Inf in foreign function call (arg 1) Can anybody please explain why this is the case (and how to avoid this problem)? I should further add, that I know that

I have a database metrics grouped by day, and I need to forecast the data for the next 3 months. These data have seasonality, (I believe that the seasonality is by days of the week). I want to use the Holt Winters method using R, I need to create a time series object, which asks for frequency, (That I think is 7). But how can I know if I'm sure? Have a function to identify the best frequency? I'm using: FID_TS <- ts(FID_DataSet$Value, frequency=7) FID_TS_Observed <- HoltWinters(FID_TS) If I decompose this data with decompose(FID_TS) , I have: And this is my first forecast FID_TS_Observed :

I am pretty sure I am missing something which is very simple but still not able to figure out why this error is showing up . The data I have is of every month end data from 2013 Apr to 2014 Mar. Now I want to understand the trend over the 12 months period . xx <- structure(c(41.52, 41.52, 41.52, 41.68, 41.68, 41.68, 41.84, 41.84, 41.84, 42.05, 42.05, 42.05), .Tsp = c(2013.25, 2014.16666666667, 12), class = "ts"); is my time series data . Now when I use stl(xx,s.window ="periodic") I get error : Error in stl(xx, s.window = "periodic") : series is not periodic or has less than two periods i am

I'm going to plot a line chart that will change from solid line to dashed line to to indicate real data and forecasting data. I'm not sure if i need to extend some of the classes like XYLineAndShapeRenderer or something else, or maybe there is some convenient way to achieve this? Here is a demostration graph i plotted using Excel. I am talking about the gray lines in the graph. That is what i want. I don't know if there is a renderer that allow me to indicate which range dashed or solid There are two ways I can think of for doing this. Neither of them are elegant, but that may be the reality

I know that MAPE and WMAPE as a forecast error metrics, they have some benefits. But what's the gaps? Someone says: For MAPE: "Combinations with very small or zero volumes can cause large skew in results" And for WMAPE: "Combinations with large weights can skew the results in their favor" I can't understand, can anyone explain the two statements for the weakness of the two metrics? Thanks. For MAPE, Mean absolute percentage error [1], suppose we denote the actual value with A , and predicted value with P . You have a series of data at time 1 thru n, then MAPE = 100/n * ( Sum of |(A(t) - P(t))