Aditya Limaye
06/15/2023, 4:30 PMn_freq_downsample
. i noticed in the AutoNHITS default_config definition, there is a tune.choice over the following values:
"n_freq_downsample": tune.choice(
[
[168, 24, 1],
[24, 12, 1],
[180, 60, 1],
[60, 8, 1],
[40, 20, 1],
[1, 1, 1],
]
),
do you all have any intuition about whether lining up these frequencies with known natural frequencies of the data is useful for performance? for example, [168, 24, 1] seems to correspond to weekly (24 x 7) , daily (24 x 1), and hourly frequencies.
the reason i ask is as follows: let's say i have an NHITS model that predicts hourly-sampled data, and i find through the course of hyperparameter optimization that n_freq_downsample=[168, 24, 1]
is most performant. if i was then to train a model that predicts the same series, but now sampled at 10minute frequency (6 samples per hour), should i then change my hyperparameter search space to include a choice for n_freq_downsample = [168*6, 24*6, 6]
?
any insight you might have would be appreciated - thanks in advance!Kin Gtz. Olivares
06/15/2023, 4:37 PMAditya Limaye
06/15/2023, 4:38 PMManuel
06/18/2023, 7:35 PMKin Gtz. Olivares
06/19/2023, 12:26 PM