[This article was first published on

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

**Systematic Investor » R**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

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

- Time series cross-validation 4: forecasting the S&P 500
- Holt-Winters forecast using ggplot2
- Autoplot: Graphical Methods with ggplot2
- Large-Scale Parallel Statistical Forecasting Computations in R (2011) by M. Stokely, F. Rohani, E. Tassone
- Forecasting time series data
- ARIMA Sector Forecasts

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)])), border=NA, col=col.add.alpha(i+2,150)) } 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 #****************************************************************** load.packages('forecast,fGarch,fArma') 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.

To

**leave a comment**for the author, please follow the link and comment on their blog:**Systematic Investor » R**.R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.