Meningkatkan model Nabi yang ditafsirkan dengan deep learning
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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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nprophet_model.plot_components(preds_df_2, residuals=True);
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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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