# Yet Another Forecast Dashboard

July 30, 2012
By

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Recently, I came across quite a few examples of time series forecasting using R. Here are some examples:

So I have decided to roll my own version of Forecast Dashboard as well. First, let’s define some helper functions:

```###############################################################################
# Load Systematic Investor Toolbox (SIT)
# http://systematicinvestor.wordpress.com/systematic-investor-toolbox/
###############################################################################
setInternet2(TRUE)
con = gzcon(url('http://www.systematicportfolio.com/sit.gz', 'rb'))
source(con)
close(con)

# extract forecast info
forecast.helper <- function(fit, h=10, level = c(80,95)) {
out = try(forecast(fit, h=h, level=level), silent=TRUE)
if (class(out)[1] != 'try-error') {
out = data.frame(out)
} else {
temp = data.frame(predict(fit, n.ahead=h, doplot=F))
pred = temp[,1]
se = temp[,2]
qq = qnorm(0.5 * (1 + level/100))
out = matrix(NA, nr=h, nc=1+2*len(qq))
out[,1] = pred
for(i in 1:len(qq))
out[,(2*i):(2*i+1)] = c(pred - qq[i] * se, pred + qq[i] * se)
colnames(out) = c('Point.Forecast', matrix(c(paste('Lo', level, sep='.'), paste('Hi', level, sep='.')), nr=2, byrow=T))
out = data.frame(out)
}
return(out)
}

# compute future dates for the forecast
forecast2xts <- function(data, forecast) {
# length of the forecast
h = nrow(forecast)
dates = as.Date(index(data))

new.dates = seq(last(dates)+1, last(dates) + 2*365, by='day')
rm.index = date.dayofweek(new.dates) == 6 | date.dayofweek(new.dates) == 0
new.dates = new.dates[!rm.index]

new.dates = new.dates[1:h]
return(make.xts(forecast, new.dates))
}

# create forecast plot
forecast.plot <- function(data, forecast, ...) {
out = forecast2xts(data, forecast)

# create plot
plota(c(data, out[,1]*NA), type='l',
ylim = range(data,out,na.rm=T), ...)

# highligh sections
new.dates = index(out)
temp = coredata(out)

n = (ncol(out) %/% 2)
for(i in n : 1) {
polygon(c(new.dates,rev(new.dates)),
c(temp[,(2*i)], rev(temp[,(2*i+1)])),
}

plota.lines(out[,1], col='red')

labels = c('Data,Forecast', paste(gsub('Lo.', '', colnames(out)[2*(n:1)]), '%', sep=''))
plota.legend(labels, fill = c('black,red',col.add.alpha((1:n)+2, 150)))
}
```

Now we are ready to fit time series models and create a sample Forecast Dashboard. Below are some examples:

```	#*****************************************************************
# Create models
#******************************************************************

sample = last(data\$prices\$SPY, 200)
ts.sample = ts(sample, frequency = 12)

models = list(
# fGarch
garch = garchFit(~arma(1,1)+garch(1,1), data=sample, trace=F),
# fArma
arima = armaFit(~ arima(1, 1, 1), data=ts.sample),

# forecast
arma = Arima(ts.sample, c(1,0,1)),
arfima = arfima(ts.sample),
auto.arima = auto.arima(ts.sample),

bats = bats(ts.sample),
HoltWinters = HoltWinters(ts.sample),
naive = Arima(ts.sample, c(0,1,0))
)

#*****************************************************************
# Create Report
#******************************************************************
# 30 day forecast with 80% and 95% confidence bands
layout(matrix(1:9,nr=3))
for(i in 1:len(models)) {
out = forecast.helper(models[[i]], 30, level = c(80,95))
forecast.plot(sample, out, main = names(models)[i])
}

# 30 day forecast with 75%,85%,95%,97%, and 99% confidence bands
layout(matrix(1:9,nr=3))
for(i in 1:len(models)) {
out = forecast.helper(models[[i]], 30, level = c(75,85,95,97,99))
forecast.plot(sample, out, main = names(models)[i])
}
```

I encourage you to read more about various time series models available in R and share your examples of Forecast Dashboards.

To view the complete source code for this example, please have a look at the bt.forecast.dashboard() function in bt.test.r at github.

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