Clever Geek Handbook

Facebook Prophet + Deep Learning = NeuralProphet

Verbesserung eines interpretierten Prophetenmodells durch tiefes Lernen

Hallo, Chabrowiten. Im Rahmen der Rekrutierung von Studenten fĂŒr den Online-Kurs " Maschinelles Lernen. Fortgeschritten " haben wir eine Übersetzung des Materials vorbereitet.



Wir laden alle ein, an der offenen Demo-Lektion "Clustering Time Series" teilzunehmen : eine interessante Aufgabe, die mit Zeitreihen verknĂŒpft werden kann.

‱ Ist es möglich, an der Börse Ă€hnliche finanzielle Vermögenswerte in ihrer Dynamik zu finden?

‱ Wie gruppieren Sie Benutzer nach ihrem Verhalten?

‱ Wer hat Roger Rabbit gerahmt?

Wir werden Antworten auf einige dieser Fragen in einer Online-Lektion erhalten. Begleiten Sie uns!





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import pandas as pd
from fbprophet import Prophet
from neuralprophet import NeuralProphet
from sklearn.metrics import mean_squared_error

# plotting
import matplotlib.pyplot as plt

# settings
plt.style.use('seaborn')
plt.rcParams["figure.figsize"] = (16, 8)

neural_prophet_1.py hosted with ❀ by GitHub





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# loading the dataset
df = pd.read_csv('../neural_prophet/example_data/wp_log_peyton_manning.csv')
print(f'The dataset contains {len(df)} observations.')
df.head()

neural_prophet_2.py hosted with ❀ by GitHub





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df.plot(x='ds', y='y', title='Log daily page views');
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    
# getting the train/test split
test_length = 365
df_train = df.iloc[:-test_length]
df_test = df.iloc[-test_length:]

neural_prophet_3.py hosted with ❀ by GitHub





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prophet_model = Prophet()
prophet_model.fit(df_train)
future_df = prophet_model.make_future_dataframe(periods=test_length)
preds_df_1 = prophet_model.predict(future_df)

neural_prophet_4.py hosted with ❀ by GitHub





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nprophet_model = NeuralProphet()
metrics = nprophet_model.fit(df_train, freq="D")
future_df = nprophet_model.make_future_dataframe(df_train, 
                                                 periods = test_length, 
                                                 n_historic_predictions=len(df_train))
preds_df_2 = nprophet_model.predict(future_df)

neural_prophet_5.py hosted with ❀ by GitHub





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# prepping the DataFrame
df_test['prophet'] = preds_df_1.iloc[-test_length:].loc[:, 'yhat']
df_test['neural_prophet'] = preds_df_2.iloc[-test_length:].loc[:, 'yhat1']
df_test.set_index('ds', inplace=True)

print('MSE comparison ----')
print(f"Prophet:\t{mean_squared_error(df_test['y'], preds_df_1.iloc[-test_length:]['yhat']):.4f}")
print(f"NeuralProphet:\t{mean_squared_error(df_test['y'], preds_df_2.iloc[-test_length:]['yhat1']):.4f}")

df_test.plot(title='Forecast evaluation');

neural_prophet_6.py hosted with ❀ by GitHub





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"Machine Learning. Advanced"





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