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Can you explain more about max_steps parameter in NHITS? As I increase the no. of max_steps (from 1 to 100), the model gets more accurate, however, I am unable to replicate my predictions with higher no. of max_steps.

I ran the following code multiple times -

nhits = NHITS(h=56,
              input_size=560,
              max_steps=1,
              random_seed=42,)
fcst = NeuralForecast(models=[nhits],freq='D')
fcst.fit(df=train_fin)
pred_nhits = fcst.predict(random_seed=42)

I ended up getting the same error percentage when comparing predictions and actuals for the next 56 days: 36.01%

When I ran the following code multiple times -

nhits = NHITS(h=56,
              input_size=560,
              max_steps=100,
              random_seed=42,)
fcst = NeuralForecast(models=[nhits],freq='D')
fcst.fit(df=train_fin)
pred_nhits = fcst.predict(random_seed=42)

I get different error percentages each time as the predictions are different each time. However the error percentages are quite low ranging between 0.1 to 2%. Model does really well!

It would be very helpful if you can recommend a way I can get stable results as well as lower error rates. Thankyou!