{
h_exo = ifelse(simtype=="mix", h[2], h)
- # TODO: [rnormand] if predict_at == 0h then we should use measures from day minus 1
- M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo_hat) )
- M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo_hat) )
+ M = matrix( nrow=1+length(fdays_indices), ncol=1+length(dat[[today]]$exo) )
+ M[1,] = c( dat[[today]]$level, as.double(dat[[today]]$exo) )
for (i in seq_along(fdays_indices))
{
M[i+1,] = c( dat[[ fdays_indices[i] ]]$level,
- as.double(dat[[ fdays_indices[i] ]]$exo_hat) )
+ as.double(dat[[ fdays_indices[i] ]]$exo) )
}
sigma = cov(M) #NOTE: robust covariance is way too slow