本期作者:Eric Brown本期编辑:Allen | 崙系列1:我用Facebook开源神器Prophet,预测时间序列基于Python数据基于标普500指数:import pandas as pdimport numpy as npfrom fbprophet import Prophetimport matplotlib.pyplot as plt %matplotlib inline plt.rcParams['figure.figsize']=(20,10)plt.style.use('ggplot')market_df = pd.read_csv('../examples/SP500.csv', index_col='DATE', parse_dates=True)df = market_df.reset_index().rename(columns={'DATE':'ds', 'SP500':'y'})df['y'] = np.log(df['y']) #lets take a look at our data quicklydf.set_index('ds').y.plot()运行Prophetmodel = Prophet()model.fit(df);future = model.make_future_dataframe(periods=366)forecast = model.predict(future)Prophet已经创建了所需的模型并匹配数据。Prophet在默认情况下为我们创建了变化点并将它们存储在.changepoints中。默认情况下,Prophet在初始数据
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