manning

# 使用Prophet预测manning未来365天的页面流量 # 从2007年12月10日开始 import pandas as pd from fbprophet import Prophet import matplotlib.pyplot as plt %matplotlib inline # 读入数据集 df = pd.read_csv('./manning.csv') print(df.head()) print(df.tail()) # 拟合模型 model = Prophet() model.fit(df) # 构建待预测日期数据框，periods = 365 代表除历史数据的日期外再往后推 365 天 future = model.make_future_dataframe(periods=365) print(future.tail()) # 预测数据集 forecast = model.predict(future) #print(forecast.columns) print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()) # 展示预测结果 model.plot(forecast) plt.show() # 使用Prophet预测manning未来365天的页面流量 # 从2007年12月10日开始 import pandas as pd from fbprophet import Prophet import matplotlib.pyplot as plt %matplotlib inline # 读入数据集 df = pd.read_csv('./manning.csv') print(df.head()) print(df.tail()) # 拟合模型 model = Prophet() model.fit(df) # 构建待预测日期数据框，periods = 365 代表除历史数据的日期外再往后推 365 天 future = model.make_future_dataframe(periods=365) print(future.tail()) # 预测数据集 forecast = model.predict(future) #print(forecast.columns) print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()) # 展示预测结果 model.plot(forecast) plt.show() # 预测的成分分析绘图，展示预测中的趋势、周效应和年度效应 model.plot_components(forecast) print(forecast.columns) # 饱和增长 df['cap'] = 8.5 m = Prophet(growth='logistic') m.fit(df) # 预测未来 5 年的数据 future = m.make_future_dataframe(periods=1826) # 将未来的承载能力设定得和历史数据一样 future['cap'] = 8.5 fcst = m.predict(future) fig = m.plot(fcst) print(future) # 预测饱和减少 df['y'] = 10 - df['y'] df['cap'] = 6 # 设置下限 df['floor'] = 1.5 future['cap'] = 6 future['floor'] = 1.5 m = Prophet(growth='logistic') m.fit(df) fcst = m.predict(future) fig = m.plot(fcst) from fbprophet.plot import add_changepoints_to_plot fig = m.plot(forecast) # plt.gca()获得当前的Axes对象ax # 获取显著的突变点的位置 a = add_changepoints_to_plot(fig.gca(), m, forecast) print(m) # 指定突变点的位置 m = Prophet(changepoints=['2014-01-01']) m.fit(df) future = m.make_future_dataframe(periods=365) forecast = m.predict(future) m.plot(forecast); # 对节假日建模 # 将节日看成是一个正态分布，把活动期间当做波峰，lower_window 以及upper_window 的窗口作为扩散 playoffs = pd.DataFrame({ 'holiday': 'playoff', 'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16', '2010-01-24', '2010-02-07', '2011-01-08', '2013-01-12', '2014-01-12', '2014-01-19', '2014-02-02', '2015-01-11', '2016-01-17', '2016-01-24', '2016-02-07']), 'lower_window': 0, 'upper_window': 1, }) superbowls = pd.DataFrame({ 'holiday': 'superbowl', 'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']), 'lower_window': 0, 'upper_window': 1, }) holidays = pd.concat((playoffs, superbowls)) m = Prophet(holidays=holidays) m.fit(df) future = m.make_future_dataframe(periods=365) forecast = m.predict(future) #print(forecast) # 通过 forecast 数据框，展示节假日效应 forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][['ds', 'playoff', 'superbowl']][-10:] # 可以使用 plot_forecast_component（从fbprophet.plot导入）来画出独立的节假日的成分 m.plot_components(forecast) #from fbprophet.plot import plot_forecast_component #m.plot_forecast_component(forecast, 'superbowl') # 预测的成分分析绘图，展示预测中的趋势、周效应和年度效应 model.plot_components(forecast) print(forecast.columns)