Description: A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "Short-term heat load forecasting for single family houses" <doi:10.1016/j.enbuild.2013.04.022>.
Description: A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "Short-term heat load forecasting for single family houses" <doi:10.1016/j.enbuild.2013.04.022>.
@@ -292,7 +292,9 @@ forecastmodel <- R6::R6Class("forecastmodel", public = list(
...
@@ -292,7 +292,9 @@ forecastmodel <- R6::R6Class("forecastmodel", public = list(
stop("The input variable '",nm,"' doesn't have the same number of observations as time vector 't' in the data. It has ",length(data[[nm]]),", but 't' has ",length(data$t))
stop("The input variable '",nm,"' doesn't have the same number of observations as time vector 't' in the data. It has ",length(data[[nm]]),", but 't' has ",length(data$t))
}
}
}else{
}else{
stop("The variable '",nm,"' is missing in data, or it has the wrong class.\nIt must be class: data.frame, matrix or vector.\nIt is needed for the input expression '",self$inputs[[i]]$expr[[1]],"'")
if(!nm=="pi"){
stop("The variable '",nm,"' is missing in data, or it has the wrong class.\nIt must be class: data.frame, matrix or vector.\nIt is needed for the input expression '",self$inputs[[i]]$expr[[1]],"'")