Predicting beyond n_forecasts with make_future_dataframe

[MonacoFlori]

I have also a problem with the make_future_dataframe function of NeuralProphet: As soon as I add a future regressor, the function does not create a dataframe that goes into the future for more than one period (in my case a day). Therefore, I can also not add the dataframe containing the future regressors values.

I tried everything I found in the documentation and also the rest I found in the discussions but nothing works for me. If I add n_forecasts to the model, it looses massively in accuracy, and in the end it will also not create the right dataframe.

[ourownstory]

Hi @MonacoFlori
What you are describing here is driven by the addition of lags (AR) - not future regressor.
Adding lagged observations (AR or lagged regressor) requires the model to have access to recent observations.
Thus a forecast can only be made for the specified n_forecasts.

You can make more predictions by 'unrolling' the model - predict, then add the predictions as (estimated) y values of the future to the dataframe, extend with make-future-dataframe into even further future, predict again, repeat. You can define a loop to unroll as you like.
We did not define this as this should be done intentionally.

And yes, the higher n_forecasts, the lower accuracy is - as you are directly regressing onto a more distant future.
I suggest you do the above suggested loop with a more reasonable n_forecasts - 0.5-1x n_lags (in your case n_forecasts=6-12) and unroll as needed. You will observe that the further you unroll, accuracy decreases.

I hope this helps!

[MonacoFlori]

Hi @ourownstory ,

thank you for your quick answer. I´m still trying to understand: If I remove the lags, is it then possible to use the make_future_dataframe with the future regressor, if I add a dataframe with (estimated) values of the future regressor for the timeframe I want to predict?
Or is the lagged regressor "included" if I add the future_regressor, because I also added values of the "future regressor" that are not in the timeframe I want to predict, but in the past, to train the model? Sorry, I´m a bit confused....

[ourownstory]

I´m still trying to understand: If I remove the lags, is it then possible to use the make_future_dataframe with the future regressor, if I add a dataframe with (estimated) values of the future regressor for the timeframe I want to predict?

If I get your Q right, yes.

Or is the lagged regressor "included" if I add the future_regressor, because I also added values of the "future regressor" that are not in the timeframe I want to predict, but in the past, to train the model? Sorry, I´m a bit confused....

At prediction time, the model needs any inputs that it was trained with. if if was trained with AR, then it needs the preceeding n_lags of observations before your prediction starts, and then predicts N-forecast next steps - identical to training.

Without lags, it is an entirely different model that is only dependent on timestamps (and any future regressors/events), and thus can be predicted for an arbitrary number of periods - independent of training.

[MonacoFlori]

I tried removing the lags, now I don´t get an error anymore, but I also get no prediction. I predicted the future regressor with another model, this is added to the dataframe but no prediction. I also do not understand how I can unroll the model, becuse there is no prediction at all. But how I understand it, I do need a prediction also for unrolling it. I also do not get how to add the lags.
I´m very sorry, but I just started with this so I am not too deep into the tuning of the code....

[ourownstory]

Without lags, unrolling is not necessary.
Also, setting n_forecasts and ar_layers is only relevant with lags. try removing those.

If you are getting no predictions, please share a minimal code snipped that I can use to reproduce this and see what's wrong. Thank you!

[MonacoFlori]

That is the part of the code which I think is relevant. I hope you can help me, but I do not understand how to add the future regressor. Do I also have a lagged regressor or is it not possible to use them both at the same time? I´m very sorry to bother you this much but I do not get the connection between the different params.

df = yf.download("AAPL", start="2019-01-02", end="2024-01-01")

df.drop(["Open", "High", "Low", "Adj Close", "Volume"], axis = 1, inplace=True)
df.reset_index(inplace=True, col_level=0)
df.columns = ["ds", "y"]

Add a future regressor

df_fut = yf.download("AAPL", start="2019-01-02", end="2024-01-01")
df_fut.drop(["Open", "High", "Low", "Adj Close"], axis = 1, inplace=True)
df_fut.reset_index(inplace=True, col_level=0)
df_fut.columns = ["ds", "y", "Volume"]

m7 = NeuralProphet(n_changepoints = 12,
yearly_seasonality = True,
weekly_seasonality = True,
daily_seasonality = True,
n_lags = 20,
n_forecasts = 20)
set_random_seed(0)
df_train, df_test = m7.split_df(df_vol, valid_p = 0.2)
m7.add_future_regressor("Volume")
metrics_7 = m7.fit(df_train, validation_df=df_test, progress=None, freq = "D", epochs = 500)

vol_pred = yf.download("AAPL", start="2019-01-02", end="2024-01-01")
vol_pred.drop(["Open", "High", "Low", "Adj Close", "Close"], axis = 1, inplace=True)
vol_pred.reset_index(inplace=True, col_level=0)
vol_pred.columns = ["ds", "y"]

model_vol = NeuralProphet(n_changepoints = 25,
yearly_seasonality = True,
weekly_seasonality = True,
daily_seasonality = True,
n_lags = 12,
n_forecasts = 30)

metrics_model_vol = model_vol.fit(vol_pred)

future = model_vol.make_future_dataframe(vol_pred, n_historic_predictions = False, periods = 90)
forecast_vol = model_vol.predict(future)

def extract_yhat(forecasts, size):
columns = forecast_vol.columns[3:]
newframe = forecast_vol[['ds', 'yhat1']].iloc[-size:].copy()
for col in columns:
if 'yhat' in col:
newframe['yhat1'] = newframe['yhat1'].fillna(forecast_vol[col])
return newframe

volume_pred = extract_yhat(forecast_vol, 31)

volume_pred.columns = ["ds", "Volume"]

df_end = pd.concat(objs = [df_fut, volume_pred], axis = 0)

future1 = m7.make_future_dataframe(df_end, n_historic_predictions = True, periods = 30)
forecast_end = m7.predict(future1)

[MonacoFlori]

I´m sorry that the code is so much, I thought you would need it to recreate the error.
The main part of NeuralProphet would be:

m7 = NeuralProphet(n_changepoints = 12,
yearly_seasonality = True,
weekly_seasonality = True,
daily_seasonality = True,
n_lags = 20,
n_forecasts = 20)
set_random_seed(0)
m7.add_future_regressor("Volume")
metrics_7 = m7.fit(df, progress=None, freq = "D", epochs = 500)

future1 = m7.make_future_dataframe(df, regressors_df = df_vol, n_historic_predictions = True, periods = 30)
forecast = m7.predict(future1)

I am quite desperate as I need it for a project at my data science training

[ourownstory]

@MonacoFlori Very sorry, but I am currently not able to help you much with this, but you may find some of the principles I tried to point to explained in this great resource: https://otexts.com/fpp3/