Funggcast Function to Plot Time Series and Predicted Series With Forecast on the Same Ggplot

I have this time series data which I want to plot its series, its predicted series, and its forecast on the same ggplot. I got this function here that demonstrates what I want but I have a date Issue.

#----------Simulate an Arima (3, 1, 4) Process-------------
library(forecast)

y <- arima.sim(model=list(order=c(3, 1, 4),ar=c(0.7662, 0.8768, - 0.7183), ma = c(- 0.0256, - 0.9333, 0.0041, - 0.0207)),n = 42)
y <- ts(y,freq=1,start=c(1980))

#-- Extract Training Data, Fit the Wrong Model, and Forecast
yt<-window(y,end=2020)

yfit<-Arima(yt,order=c(3, 1, 4))

yfor<-forecast(yfit)

#---Extract the Data for ggplot using funggcast()
funggcast <- function(dn, fcast){
  
  en <- max(time(fcast$mean)) # Extract the max date used in the forecast
  
  # Extract Source and Training Data
  ds <- as.data.frame(window(dn, end = en))
  names(ds) <- 'observed'
  ds$date <- as.Date(time(window(dn, end = en)))
  
  # Extract the Fitted Values (need to figure out how to grab confidence intervals)
  dfit <- as.data.frame(fcast$fitted)
  dfit$date <- as.Date(time(fcast$fitted))
  names(dfit)[1] <- 'fitted'
  
  ds <- merge(ds, dfit, all.x = T) # Merge fitted values with source and training data
  
  # Extract the Forecast values and confidence intervals
  dfcastn <- as.data.frame(fcast)
  dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-"))
  names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date')
  
  pd <- merge(ds, dfcastn,all= T) # final data.frame for use in ggplot
  return(pd)
  
} 
pd<-funggcast(y,yfor)

#---Plot in ggplot2 
library(ggplot2)
library(scales)


p1a<-ggplot(data=pd,aes(x=date,y=observed)) 
p1a<-p1a+geom_line(col='red')
p1a<-p1a+geom_line(aes(y=fitted),col='blue')
p1a<-p1a+geom_line(aes(y=forecast))+geom_ribbon(aes(ymin=lo95,ymax=hi95),alpha=.25)
p1a<-p1a+scale_x_date(name='',breaks='1 year',minor_breaks='1 month',labels=date_format("%b-%y"),expand=c(0,0))
p1a<-p1a+scale_y_continuous(name='Units of Y')
p1a<-p1a+opts(axis.text.x=theme_text(size=10),title='Arima Fit to Simulated Data\n (black=forecast, blue=fitted, red=data, shadow=95% conf. interval)')

I get this Error Message:

pd<-funggcast(y,yfor)
Error in as.Date.default(time(window(dn, end = en))) :
do not know how to convert ‘time(window(dn, end = en))’ to class “Date”

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>Solution :

You get the error because you did not load zoo package, I removed opts in ggplot:

library(forecast)
library(zoo)

y <- arima.sim(model=list(order=c(3, 1, 4),ar=c(0.7662, 0.8768, - 0.7183), ma = c(- 0.0256, - 0.9333, 0.0041, - 0.0207)),n = 42)
y <- ts(y,freq=1,start=c(1980))
               

#-- Extract Training Data, Fit the Wrong Model, and Forecast
yt<-window(y,end=2020)

yfit<-Arima(yt,order=c(3, 1, 4))

yfor<-forecast(yfit)

#---Extract the Data for ggplot using funggcast()
funggcast <- function(dn, fcast){
  
  en <- max(time(fcast$mean)) # Extract the max date used in the forecast
  
  # Extract Source and Training Data
  ds <- as.data.frame(window(dn, end = en))
  names(ds) <- 'observed'
  ds$date <- as.Date(time(window(dn, end = en)))
  
  # Extract the Fitted Values (need to figure out how to grab confidence intervals)
  dfit <- as.data.frame(fcast$fitted)
  dfit$date <- as.Date(time(fcast$fitted))
  names(dfit)[1] <- 'fitted'
  
  ds <- merge(ds, dfit, all.x = T) # Merge fitted values with source and training data
  
  # Extract the Forecast values and confidence intervals
  dfcastn <- as.data.frame(fcast)
  dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-"))
  names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date')
  
  pd <- merge(ds, dfcastn,all= T) # final data.frame for use in ggplot
  return(pd)
  
} 

pd<-funggcast(y,yfor)

#---Plot in ggplot2 
library(ggplot2)
library(scales)

ggplot(data=pd,aes(x=date,y=observed)) +
  geom_line(col='red')+
  geom_line(aes(y=fitted),col='blue')+
  geom_line(aes(y=forecast))+geom_ribbon(aes(ymin=lo95,ymax=hi95),alpha=.25)+
  scale_x_date(date_labels = "%b-%y",date_breaks = "1 year",date_minor_breaks = "1 month")+
  scale_y_continuous(name='Units of Y')+
  theme(axis.text.x=element_text(angle = 45, vjust = 0.5, hjust=1),
        plot.title=element_text(hjust=0.5))+
  ggtitle('Arima Fit to Simulated Data\n (black=forecast, blue=fitted, red=data, shadow=95% conf. interval)')