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!