Unobserved Components Model Predictions: predict.UCM not working in rucm package - r

Question

I built a model using ucm function. However when i try to forecast for future, it's not letting me pass the independent variables.
library("rucm")
library("lubridate")
#Create Simulated Data
Date<- as.Date(seq(from=as.Date('2012-01-01'),to=as.Date('2014-03-31'),by=1),"%Y-%m-%d")
Actual <- sample(27:65,length(Date),replace = TRUE)
DOW <- wday(Date)
Month <- month(Date)
DOM <- mday(Date)
Week <- week(Date)
Ya <- year(Date)
Ya <- ifelse(Ya=="2014",2,1)
a <- data.frame(Date,Actual,DOW,Month,DOM,Week,Ya)
a$Date<-as.Date(a$Date,"%Y-%m-%d")
abc <- cbind(Weekday=model.matrix(~as.factor(a$DOW)),
Mont=model.matrix(~as.factor(a$Month)),
Day=model.matrix(~as.factor(a$DOM)),a[,7,drop=FALSE],
Weekofyear=model.matrix(~as.factor(a$Week)))
abc<-data.frame(abc)
abc<-data.frame(abc[,c(-1,-8,-20,-52)])
abc2 <- subset(abc,abc$Ya==1)
abc2 <- abc2[,-48]
abc3 <- subset(abc,abc$Ya==2)
abc3 <- abc3[,-48]
#train and insample MAPE
a1<-subset(a,a$Ya==1)
a2<-subset(a,a$Ya==2)
#build model
dat <- as.data.frame(cbind(a1[,2,drop=FALSE], abc2))
fo <- as.formula(paste("Actual ~ ", paste(names(dat)[2:42], collapse= "+")))
fit_train_ucm <- ucm(fo, data = dat, cycle = TRUE, cycle.period = 365)
#predict for future
predict(fit_train_ucm,n.ahead = 90,newdata = abc3)
i am getting this error
Error in is.SSModel(newdata, na.check = TRUE, return.logical = FALSE) :
Object is not of class 'SSModel'
Update:
Based on suggestion by package author from GitHub, i used below code(sorry to say this is not very self explanatory, i did however try to edit code)
SSModel(rep(NA,nrow(abc3)) ~ x + SSMtrend(2, Q = list(fit_train_ucm$est.var.level, fit_train_ucm$est.var.slope)) + SSMseasonal(12, Q = fit_train_ucm$est.var.season), H = fit_train_ucm$irr.var, data=abc3)
Now the error message is
Error in eval(expr, envir, enclos) : object 'x' not found
Any help on this?

Answer 1

This bug of prediction with newdata in ucm is yet to corrected.
This is how you can get predictions for an out-of-sample period.
indep <- paste(names(dat)[2:42], collapse= "+")
newdata1 <- SSModel(as.formula(paste0("rep(NA,nrow(abc3)) ~ ", indep, "+ SSMtrend(1, Q = list(fit_train_ucm$est.var.level))",
"+ SSMcycle(365, Q = fit_train_ucm$est.var.cycle)")), H = fit_train_ucm$irr.var, data=abc3)
pred<-predict(fit_train_ucm$model, newdata=newdata1)
So basically here I am using the predict function from KFAS package. To use the predict function, I have to define the data to be an object of class SSModel.
You will take all the parameters that you used in the model as independent variables and run a State space model with NA's as independent variable.
P.S.: I will keep the comments in mind to write a better answer next time.

Answer 2

Just so someone lands at this page again in future - there has been some update on the git repo maintained by the author to approach this problem in simplistic way. Please check this commit
Alternatively, pasting the function which one may use from that commit:
predict.ucm <- function(object, n.ahead, newdata,...){
#### Predict in sample ####
if (missing(newdata)) {
return(predict(object = object$model, n.ahead = n.ahead))
}
#### Predict out of sample ####
# Regression variables.
model_variables <- paste0(names(object$est), collapse = " + ")
# Trend
# Case 1 no trend
if (is.null(object$est.var.level) & is.null(object$est.var.slope)) {
model_trend <- ""
}
# Case 2 level and trend
if (!is.null(object$est.var.level) & !is.null(object$est.var.slope)) {
model_trend <- "+ SSMtrend(degree = 2, Q = list(object$est.var.level, object$est.var.slope))"
}
# Case 3 level only / trend only is not allowed in R
if (!is.null(object$est.var.level) & is.null(object$est.var.slope)) {
model_trend <- "+ SSMtrend(degree = 1, Q = list(object$est.var.level))"
}
# Seasonality
if (!is.null(object$est.var.season)) {
model_season <- sprintf("+ SSMseasonal(period = %s, Q = object$est.var.season)",
object$call['season.length'] %>% as.character())
} else {
model_season <- ""
}
# Cycle
if (!is.null(object$est.var.cycle)) {
model_cycle <- sprintf("+ SSMcycle(period = %s, Q = object$est.var.cycle)",
object$call['cycle.period'] %>% as.character())
} else {
model_cycle <- ""
}
# Combine all components into a formula
model_formula <- as.formula(sprintf("rep(NA,nrow(newdata)) ~ %s %s %s %s",
model_variables,
model_trend,
model_season,
model_cycle
))
# Build a SSM object for the prediction
oos_data <- KFAS::SSModel(formula = model_formula, H = object$irr.var, data = newdata)
# Return the predictions
predict(object$model, newdata = oos_data)
}
EDIT
In case you are interested in predicting on a new data which is considered as for the same time period as the original model was built using, then following edit can be made in the above function. That new data should ideally include the same dependent variable values which was used to build the first model.
# Using same dependent which was used initially to fit the estimates, instead of NA
model_formula <- as.formula(paste0(
object$model$terms[[2]],
sprintf(
" ~ %s %s %s %s",
model_variables,
model_trend,
model_season,
model_cycle
)
))
# Build a SSM object for the prediction
oos_data <- KFAS::SSModel(formula = model_formula, H = object$irr.var, data = newdata)
# Return the predictions for in sample only - hence removing newdata arg
# predict(object$model, newdata = oos_data)
predict(oos_data)