时序预测模型fbprophet

python: 3.7.9
pystan: 2.19.0.0
pandas
fbprophet: 0.6.0
anaconda方式&＃xff1a;
conda install pystan&＃61;2.19.0.0
conda install -c conda-forge fbprophet&＃61;0.6.0

https://www.cnblogs.com/bonelee/p/9577432.html
https://facebook.github.io/prophet/docs/quick_start.html
可以预测数据&＃xff0c;也可以给出趋势。时间序列预测上&＃xff0c;充满专家经验&＃xff1a;周期趋势、离群点、突变点、突变。

make_future_dataframe中的fre是Offset aliases形式的&＃xff0c;用的pandas时间跨度
https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
比如1hour1min可以表示时间跨度是61分钟

利用函数make_future_dataframe生成未来时间DataFrame对象future &＃xff1a;

from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import mathtimelist &＃61; list(pd.date_range(start&＃61;&＃39;2021-01-01 00:00:00&＃39;, end&＃61;&＃39;2022-01-01 00:00:00&＃39;, freq&＃61;&＃39;H&＃39;))
y &＃61; [math.sin(data.hour) for k, data in enumerate(timelist)]data_df &＃61; pd.DataFrame({&＃39;ds&＃39;: timelist, &＃39;y&＃39;: y})
data_df[&＃39;ds&＃39;] &＃61; data_df[&＃39;ds&＃39;].astype(&＃39;datetime64[ns]&＃39;)m &＃61; Prophet()
m.fit(data_df) # 训练模型mfuture &＃61; m.make_future_dataframe(periods&＃61;50, freq&＃61;&＃39;H&＃39;,include_history&＃61;False) # 预测的设置 还没预测
forecast &＃61; m.predict(future) # 开始预测# plt.plot(data_df[&＃39;ds&＃39;][-50:], data_df[&＃39;y&＃39;][-50:], color&＃61;&＃39;b&＃39;)
# plt.plot(forecast[&＃39;ds&＃39;], forecast[&＃39;yhat&＃39;], color&＃61;&＃39;r&＃39;)
# plt.show()plt.plot(list(data_df[&＃39;ds&＃39;][-50:])&＃43;list(forecast[&＃39;ds&＃39;]),list(data_df[&＃39;y&＃39;][-50:])&＃43;list(forecast[&＃39;yhat&＃39;]), color&＃61;&＃39;b&＃39;)
plt.show()


未来任意时间&＃xff1a;

# 1 取数据
# https://www.kesci.com/mw/dataset/5d64a35a8499bc002c07bb1bfrom fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import mathtimelist &＃61; list(pd.date_range(start&＃61;&＃39;2021-01-01 00:00:00&＃39;, end&＃61;&＃39;2022-01-01 00:00:00&＃39;, freq&＃61;&＃39;H&＃39;))
y &＃61; [math.sin(data.hour) for k, data in enumerate(timelist)]data_df &＃61; pd.DataFrame({&＃39;ds&＃39;: timelist, &＃39;y&＃39;: y})
data_df[&＃39;ds&＃39;] &＃61; data_df[&＃39;ds&＃39;].astype(&＃39;datetime64[ns]&＃39;)m &＃61; Prophet()
m.fit(data_df) # 训练模型mfuture &＃61; pd.DataFrame({&＃39;ds&＃39;: list(pd.date_range(start&＃61;&＃39;2022-05-01 00:00:00&＃39;, end&＃61;&＃39;2022-05-05 00:00:00&＃39;, freq&＃61;&＃39;H&＃39;))}) # 预测的设置 还没预测
forecast &＃61; m.predict(future) # 开始预测# plt.plot(data_df[&＃39;ds&＃39;][-50:], data_df[&＃39;y&＃39;][-50:], color&＃61;&＃39;b&＃39;)
plt.plot(forecast[&＃39;ds&＃39;], forecast[&＃39;yhat&＃39;], color&＃61;&＃39;r&＃39;)
plt.show()# plt.plot(list(data_df[&＃39;ds&＃39;][-50:])&＃43;list(forecast[&＃39;ds&＃39;]),
# list(data_df[&＃39;y&＃39;][-50:])&＃43;list(forecast[&＃39;yhat&＃39;]), color&＃61;&＃39;b&＃39;)
# plt.show()