Til Hund - 1 year ago 142

R Question

On this website, Mr. Davenport published a function to plot an arima forecast with

`ggplot2`

Now, when I use my data, I would end with the warning:

`1: In window.default(x, ...) : 'end' value not changed`

2: In window.default(x, ...) : 'end' value not changed

I know that it happens when I call this command

`pd <- funggcast(yt, yfor)`

`end = c(2013)`

This is the code I use:

`library(ggplot2)`

library(zoo)

library(forecast)

myts <- ts(rnorm(55), start = c(1960), end = c(2013), freq = 1)

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(as.yearmon(row.names(dfcastn)))

names(dfcastn) <- c('forecast','lo80','hi80','lo95','hi95','date')

pd <- merge(ds, dfcastn,all.x = T) # final data.frame for use in ggplot

return(pd)

}

yt <- window(myts, end = c(2013)) # extract training data until last year

yfit <- auto.arima(myts) # fit arima model

yfor <- forecast(yfit) # forecast

pd <- funggcast(yt, yfor) # extract the data for ggplot using function funggcast()

ggplot(data = pd, aes(x = date,y = observed)) + geom_line(color = "red") + geom_line(aes(y = fitted), color = "blue") + geom_line(aes(y = forecast)) + geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25) + scale_x_date(name = "Time in Decades") + scale_y_continuous(name = "GDP per capita (current US$)") + theme(axis.text.x = element_text(size = 10), legend.justification=c(0,1), legend.position=c(0,1)) + ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)") + scale_color_manual(values = c("Blue", "Red"), breaks = c("Fitted", "Data", "Forecast"))

`forecast`

`ggplot2`

If I run your updated code with my data here, than I get the graph down below. Note that I did not change the

`end = c(2023)`

`mtys`

`myts <- ts(WDI_gdp_capita$Brazil, start = c(1960), end = c(2023), freq = 1)`

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 = 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.x = T) # final data.frame for use in ggplot

return(pd)

} # ggplot function by Frank Davenport

yt <- window(myts, end = c(2013)) # extract training data until last year

yfit <- auto.arima(yt) # fit arima model

yfor <- forecast(yfit) # forecast

pd <- funggcast(myts, yfor) # extract the data for ggplot using function funggcast()

ggplot(data = pd, aes(x = date, y = observed)) + geom_line(color = "red") + geom_line(aes(y = fitted), color = "blue") + geom_line(aes(y = forecast)) + geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25) + scale_x_date(name = "Time in Decades") + scale_y_continuous(name = "GDP per capita (current US$)") + theme(axis.text.x = element_text(size = 10), legend.justification=c(0,1), legend.position=c(0,1)) + ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)") + scale_color_manual(values = c("Blue", "Red"), breaks = c("Fitted", "Data", "Forecast")) + ggsave((filename = "gdp_forecast_ggplot.pdf"), width=330, height=180, units=c("mm"), dpi = 300, limitsize = TRUE)

The almost perfect graph I get:

If I set

`end = c(2013)`

`myts`

Answer Source

There are several points that are different between Mr Davenport's analysis and the plot you are trying to make. The first one is that he is comparing the the arima forecast to some observed data, which is why he trains the model on a portion of the whole time series, the training set. To do this, you should make your initial time series longer:

```
myts <- ts(rnorm(55), start = c(1960), end = c(2023), freq = 1)
```

Then at the end of your script, where you select the training up to 2013:

```
yt <- window(myts, end = c(2013)) # extract training data until last year
```

The model should be trained on the training set, not the whole time series, so you should change the yfit line to:

```
yfit <- auto.arima(yt) # fit arima model
```

And call the funggcast function using the whole time series, because it needs the observed and fitted data:

```
pd <- funggcast(myts, yfor)
```

Finally, he uses dates that have month and year, so in his `funggcast`

function, change this line:

```
dfcastn$date <- as.Date(as.yearmon(row.names(dfcastn)))
```

To:

```
dfcastn$date <- as.Date(paste(row.names(dfcastn),"01","01",sep="-"))
```

This is because the values predicted by the model need to be changed to dates, like 2014 has to be changed to 2014-01-01, in order to be merged with the observed data.

After all the changes, the code looks like this:

```
library(ggplot2)
library(zoo)
library(forecast)
myts <- ts(rnorm(55), start = c(1960), end = c(2013), freq = 1)
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)
}
yt <- window(myts, end = c(2013)) # extract training data until last year
yfit <- auto.arima(yt) # fit arima model
yfor <- forecast(yfit) # forecast
pd <- funggcast(myts, yfor) # extract the data for ggplot using function funggcast()
plotData<-ggplot(data = pd, aes(x = date, y = observed)) + geom_line(aes(color = "1")) +
geom_line(aes(y = fitted,color="2")) +
geom_line(aes(y = forecast,color="3")) +
scale_colour_manual(values=c("red", "blue","black"),labels = c("Observed", "Fitted", "Forecasted"),name="Data")+
geom_ribbon(aes(ymin = lo95, ymax = hi95), alpha = .25)+
scale_x_date(name = "Time in Decades") +
scale_y_continuous(name = "GDP per capita (current US$)")+
theme(axis.text.x = element_text(size = 10)) +
ggtitle("Arima(0,1,1) Fit and Forecast of GDP per capita for Brazil (1960-2013)")
plotData
```

And you get a plot that looks like this, the fitting is pretty bad with a completely random time series. Also ggplot will output some errors because the forecast line has no data before 2013 and the fitted data does not go on after 2013. (I ran it several times, depending on the initial, random time series, the model might just predict 0 everywhere)

Edit: changed the `pd`

assignment line as well, in case there are no observed data after 2013

Edit2: I changed the ggplot function at the end of the code to make sure the legend shows up