As stated in the book's Chapter 10 "...The exact formulation of the problem will be as follows: given data covering the previous five days and sampled once per hour, can we predict the temperature in 24 hours?.."
With this in mind do we really need to subtract 1 in :
delay <- sampling_rate * (sequence_length + 24 - 1)? (see row #108 Ch 10).
I know, this code matches the book.
But for this delay the 1st sample:
> full_df$`Date Time`[1]
[1] "2009-01-01 00:10:00 -01"
has such target:
> head(tail(full_df$`Date Time`, -delay),1)
[1] "2009-01-06 23:10:00 -01"
It is not exactly 24 hours for a prediction horizon.
Without subtracting 1 things seem to look better:
delay <- sampling_rate * (sequence_length + 24)
head(tail(full_df$`Date Time`, -delay),1)
[1] "2009-01-07 00:10:00 -01"
So i can`t figure out the reason for subtracting of 1.
Any thoughts?
As stated in the book's Chapter 10 "...The exact formulation of the problem will be as follows: given data covering the previous five days and sampled once per hour, can we predict the temperature in 24 hours?.."
With this in mind do we really need to subtract 1 in :
delay <- sampling_rate * (sequence_length + 24 - 1)? (see row #108 Ch 10).I know, this code matches the book.
But for this delay the 1st sample:
has such target:
It is not exactly 24 hours for a prediction horizon.
Without subtracting 1 things seem to look better:
So i can`t figure out the reason for subtracting of 1.
Any thoughts?