Mejora de un modelo de Profeta interpretado con aprendizaje profundo
Hola, Khabrovites. Como parte de la captación de alumnos para el curso online " Machine Learning. Avanzado ", preparamos una traducción del material.
Invitamos a todos a participar en la lección de demostración abierta "Clustering Time Series" : una tarea interesante que se puede vincular a series de tiempo.
• ¿Es posible encontrar activos financieros similares entre sí en la dinámica de la bolsa de valores?
• ¿Cómo agrupar a los usuarios según su comportamiento?
• ¿Quién mató a Roger Rabbit?
Obtendremos respuestas a algunas de estas preguntas en una lección en línea. ¡Únete a nosotros!
, Prophet, Facebook. , , , , (, ) . , .
, , — NeuralProphet. , Prophet , . , , , Prophet.
, NeuralProphet
. , , . , , . !
NeuralProphet
, NeuralProphet
, , .
, — , , . , - . , , .
Prophet . , , — . — — , . Prophet , :
Prophet — ..
— ,
— . , , . , ( ) . , , .. , . . , :
, ( ), , .
, , ,
, , ( )
, Prophet , , . NeuralProphet
— Prophet, (PyTorch Stan) (AR-Net), . AR-Net , , , , .
NeuralProphet Prophet
NeuralProphet
PyTorch,
.
.
,
( 1).
.
, , -. , , Prophet , , .
, NeuralProphet
— , pip install.
. . .
, Python.
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
Peyton Manning ( ). , , , , Prophet. — .
Facebook, , Prophet, Prophet, NeuralProphet
.
, :
# 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
, Prophet, : ds
— /, y
— , . , , .
, . , , . , , .
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
Prophet
, Prophet . 4 . . , . , . , «future dataframe». , . . , ( ). , preds_df_1
.
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
, ( ).
, . .
. .
prophet_model.plot_components(preds_df_1);
.
NeuralProphet
, API NeuralProphet
Prophet. , , , NeuralProphet
.
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
, . ( Prophet ) , «future dataframe». , .
, , .
nprophet_model.plot(preds_df_2);
. ( , preds_df_2
, preds_df_1
, , , !), .
nprophet_model.plot_components(preds_df_2, residuals=True);
, .
nprophet_model.plot_parameters();
, , plot_components ( ).
, . DataFrame
, (MSE).
# 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
.
, , MSE — NeuralProphet
.
, , . , , , , , , . . . .
NeuralProphet
— ( -), . , . Prophet, , , , .
Prophet NeuralProphet
, , , . , !
, , GitHub. , . .