Hola, buenos dias. Vengo en busco de ayuda. Estoy trabajando diferentes tipo de redes neuronales recurrentes para el pronostico de series de tiempo (LSTM-GRU y Simples) , en mi codigo estoy utilizando keras tuner para manejar diferentes hiper-parametros. Ocurre que a pensar de estar utilizando estas herramientas, tengo pronosticos demasiado malos, ya que por ejemplo en esta grafica de casos acumulados, el pronostico es una funciòn decreciente, totalmente fuera del contexto del problema. Espero puedan ayudarme Gracias

Esta es la red neurona que estoy utilizando.

Este es el codigo para mis prediciones de los casos acumulados en 60 dias. Estoy utilizando 670 datos para entrenamiento 60 datos para testeo y ventanas de 60, con batch size de 1. Antes estaba utilizando ventanas de 30 pero los pronosticos eran peoress

Hola @YEISON ARMANDO BUITRAGO LOPEZ. Por lo que veo en tu primera grafica la escala de la Y esta en 1e6, las RNN funcionan mejor escalando los datos, los estas escalando?

Nosotros tenemos ya una RNN implementada en nuestra libreria. En este notebook hay un ejemplo de como usarla: https://github.com/Nixtla/neuralforecast/blob/main/nbs/models_rnn__rnn.ipynb. Podrias probar de usar nuestra implementacion?

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Hello guys. First of all I would like to thank you for this package.

I am trying to use it for some data I got on the web.

It appeared an error

AssertionError: Mismatch in X, Y ds

when performing the following step of "getting_started.ipynb"

Y_df_forecast = model.forecast(Y_df_train, X_df= X_df)

Y_df_forecast.rename(columns={'y': 'y_hat'}, inplace=True)

Y_df_forecast.head()

I checked if Y_df_test had the

ds

correctly following Y_df_train and it has.

Any advice for debugging?

Hi @Andre P, The N-BEATSx model needs you to match the number of observations between

Y_df

and

X_df

, for the moment N-BEATSx only operates with exogenous data available at the time of the prediction. For example: calendar variables, predictions of other series. Can you check if

len(X_df)==len(Y_df)+H

, where H is the forecast horizon (n_time_out)?

Hello @Kin Gtz. Olivares. Yes,

len(X_df)==len(Y_df)+H

.

Hello again. I changed the values of output_size and if they are below 12, I can forecast, otherwise I can't

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is there an easy way to recover val / test predictions from

model_fit_predict

in the same format as

model.forecast

?

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is there any idea to improve this ?

the model can't catch up the high peak. I am using gasx model from pyflux. what is the most potential model from neural-forecast to deal with this kind of problem ?

👋 Hello, team!

I am new to neural-forecast and start trying an example: https://github.com/Nixtla/neuralforecast/blob/main/examples/getting_started.ipynb Y_df, X_df, _ = nf.data.datasets.long_horizon.LongHorizon.load('data', 'ILI') while get this error:

TypeError: sort_values() got an unexpected keyword argument 'ignore_index'

any idea what is the issue? Thanks.

Sorry for the inconvenience @Musa Would you be able to open a github issue on your finding and try try this Google Colab meanwhile?: https://colab.research.google.com/drive/1WjBbQzaivQhOldGolzymOtLmo6QX4Ieg#scrollTo=HXKT2-fpUD0Z