https://www.sarem-seitz.com/winning-with-simple-not-even-linear-time-series-models

neuralforecast_benchmark.py

from copy import deepcopy
from neuralforecast import NeuralForecast
from neuralforecast.models import NBEATS, NHITS

train_nxt = pd.DataFrame(train).reset_index()
train_nxt.columns = ["ds","y"]
train_nxt["unique_id"] = np.ones(len(train))

test_nxt = pd.DataFrame(test).reset_index()
test_nxt.columns = ["ds","y"]
test_nxt["unique_id"] = np.ones(len(test))


horizon = len(test_nxt)
models = [NBEATS(input_size=2 * horizon, h=horizon,max_epochs=50),
          NHITS(input_size=2 * horizon, h=horizon,max_epochs=50)]

nf = NeuralForecast(models=models, freq='M')
nf.fit(df=train_nxt)
Y_hat_df = nf.predict().reset_index()


nbeats = Y_hat_df["NBEATS"]
nhits = Y_hat_df["NHITS"]


rmse_simple = np.sqrt(np.mean((test.values-result_mean)**2))
rmse_nbeats = np.sqrt(np.mean((test.values-nbeats.values)**2)) 
rmse_nhits = np.sqrt(np.mean((test.values-nhits.values)**2))

pd.DataFrame([rmse_simple,rmse_nbeats,rmse_nhits], index = ["Simple", "NBEATS", "NHITS"], columns=["RMSE"])