安装Prophet

python: 3.7.9

pystan: 2.19.0.0

pandas

fbprophet: 0.6.0

anaconda方式：

conda install pystan=2.19.0.0

conda install -c conda-forge fbprophet=0.6.0

Prophet介绍

https://www.cnblogs.com/bonelee/p/9577432.html

https://facebook.github.io/prophet/docs/quick_start.html

可以预测数据，也可以给出趋势。时间序列预测上，充满专家经验：周期趋势、离群点、突变点、突变。

时间跨度

make_future_dataframe中的fre是Offset aliases形式的，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

预测未来1

利用函数make_future_dataframe生成未来时间DataFrame对象future ：

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

预测未来2

未来任意时间：

python
# 1 取数据
# https://www.kesci.com/mw/dataset/5d64a35a8499bc002c07bb1b
from fbprophet import Prophet
import pandas as pd
import matplotlib.pyplot as plt
import math
timelist = list(pd.date_range(start='2021-01-01 00:00:00', end='2022-01-01 00:00:00', freq='H'))
y = [math.sin(data.hour) for k, data in enumerate(timelist)]
data_df = pd.DataFrame({'ds': timelist, 'y': y})
data_df['ds'] = data_df['ds'].astype('datetime64[ns]')
m = Prophet()
m.fit(data_df)  # 训练模型m
future = pd.DataFrame({'ds': list(pd.date_range(start='2022-05-01 00:00:00', end='2022-05-05 00:00:00', freq='H'))})  # 预测的设置 还没预测
forecast = m.predict(future)  # 开始预测
# plt.plot(data_df['ds'][-50:], data_df['y'][-50:], color='b')
plt.plot(forecast['ds'], forecast['yhat'], color='r')
plt.show()
# plt.plot(list(data_df['ds'][-50:])+list(forecast['ds']),
#          list(data_df['y'][-50:])+list(forecast['yhat']), color='b')
# plt.show()

预测模型持久化存储