timeseries_simulator = function( DS="default", n=600, ar.cor=0.9, missing=0.1 ) {
#\\ data for development/testing
#\\ ar.cor is the autocorrelation coefficient
if (DS == "default") {
x = 1:n
y = y0 = arima.sim(n=n, model=list( ar=ar.cor ))
missing.values = sample( x, trunc(n * missing ))
y[ missing.values ] = NA
y = y + rnorm( n, mean=0 , sd=sd( y0,na.rm=TRUE ) ) # add noise
z = data.frame( x=x, y=y, y0=y0 )
z$decimal_date = 1900:(1900+n-1)
z$timestamp = lubridate::date_decimal( z$decimal_date )
}
if( DS=="sunspots") {
z = data.frame( cbind( y=c(sunspot.year), decimal_date=time(sunspot.year) ) )
z$timestamp = lubridate::date_decimal( z$decimal_date )
}
if( DS=="sunspots.seasonal") {
z = data.frame( cbind( y=c(sunspots), decimal_date=time(sunspots)))
z$timestamp = lubridate::date_decimal( z$decimal_date )
}
return (z)
}
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