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#neural-forecast
Title
# neural-forecast
a

Aditya Limaye

06/15/2023, 4:30 PM
hello! first, thanks for putting together a great library and running this community, i really appreciate it! i had a question about one of the NHITS hyperparameters,
n_freq_downsample
. i noticed in the AutoNHITS default_config definition, there is a tune.choice over the following values:
Copy code
"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!
k

Kin Gtz. Olivares

06/15/2023, 4:37 PM
Hey @Aditya Limaye, Thanks for using NeuralForecast, hope it gives you service. Your intuitions are correct regarding the downsampling hyperparameter and its effect on modelling different frequencies. Here is an example of the effects of the parameter: https://nixtla.github.io/neuralforecast/examples/signal_decomposition.html
a

Aditya Limaye

06/15/2023, 4:38 PM
very cool, thank you for pointing me to this!
m

Manuel

06/18/2023, 7:35 PM
@Kin Gtz. Olivares So for weekly data with yearly seasonality, can it make sense to use [52, 1]?
k

Kin Gtz. Olivares

06/19/2023, 12:26 PM
Using [52,1] males sense. Although that configuration is not deep for which you will have a very parsimonious model. I would keep the exploration space wide including the depth of the network.