【学习笔记-时间序列预测】prophet-使用.2变化点检测、趋势灵活性调整
1.变化点自动检测
指定增长速率可变的点来检测变化点，先对速率变化幅度进行L1正则化。默认情况下，Prophet 指定了 25 个变化点，均匀地放置在时间序列的前 80% 中。 图中的垂直线表示变化点的位置。
可以使用参数 n_changepoints 设置潜在变化点的数量，通过调整正则化可以更好地调整。 变化点的位置可以通过以下方式可视化
>>> import pystan
>>> import fbprophet
>>> import pandas as pd
>>> from fbprophet import Prophet
>>> df=pd.read_csv('D:\Machine learning\prophetmain\example_wp_log_peyton_manning.csv')
>>> m = Prophet()
>>> m.fit(df)
>>> future = m.make_future_dataframe(periods=1826)
>>> future['cap'] = 8.5
>>> forecast = m.predict(future)
>>> fig = m.plot(forecast)
>>> a = add_changepoints_to_plot(fig.gca(), m, forecast)
>>> fig.show()
默认情况下，仅在时间序列的前 80% 推断变化点，以便有足够的测试集用于预测未来趋势并避免过拟合。 此默认值适用于许多情况，但有部分情况需要使用 changepoint_range 参数进行更改。
m = Prophet(changepoint_range=0.9)2.调整趋势灵活性(trend flexibility)
如果趋势变化过拟合（灵活性太大）或欠拟合（灵活性不够），可以使用输入参数 changepoint_prior_scale 调整稀疏先验的强度。
默认情况下，此参数设置为 0.05。
欠拟合时，增加该参数使趋势更加灵活：
>>> m = Prophet(changepoint_prior_scale=0.5)
>>> forecast = m.fit(df).predict(future)
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Initial log joint probability = -130.381
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
99 5132.68 0.0184527 199.828 1 1 118
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
199 5147.36 0.00950524 88.0854 1 1 234
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
299 5160.14 0.0409634 338.996 1 1 348
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
399 5170.29 0.0032085 123.759 1 1 457
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
499 5180.91 0.0159526 142.705 0.2658 0.8149 572
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
599 5188.55 0.42206 238.495 1.457 0.1457 688
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
699 5190.65 0.00254006 50.7216 0.3708 0.3708 811
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
799 5192.52 0.000800588 63.0824 0.2513 1 923
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
899 5193.26 0.00204647 36.7975 0.4506 1 1034
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
994 5193.7 5.48253e-005 21.1011 1.232e-006 0.001 1185 LS failed, Hessian reset
999 5193.75 0.000417762 35.5305 0.2677 0.2677 1192
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1099 5194.12 0.000417867 8.15818 0.4978 0.4978 1313
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1199 5194.38 0.00264916 42.4243 0.9648 0.09648 1433
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1272 5195.26 0.000114039 51.6952 1.56e-006 0.001 1561 LS failed, Hessian reset
1299 5195.41 0.00323062 32.1101 1 1 1594
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1399 5195.66 0.000597172 27.5909 0.5719 0.5719 1715
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1468 5195.79 0.000167234 23.025 1.15e-005 0.001 1840 LS failed, Hessian reset
1499 5195.9 0.000499053 13.1386 1 1 1874
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1571 5195.96 6.84819e-005 28.4726 2.269e-006 0.001 2011 LS failed, Hessian reset
1599 5195.97 0.000134186 6.89436 2.952 0.2952 2053
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1651 5196.18 0.000347281 26.5291 8.446e-006 0.001 2159 LS failed, Hessian reset
1699 5196.34 0.00131928 22.5198 1 1 2217
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1799 5196.42 0.000475465 5.55345 1 1 2335
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1899 5196.52 5.05212e-005 15.869 0.2645 0.02645 2458
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
1999 5196.56 0.00201825 11.7838 1 1 2578
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
2099 5196.75 0.0171565 20.3801 1 1 2693
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
2199 5196.94 0.000438989 40.5423 1 1 2813
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
2299 5196.98 0.000233098 8.74493 1 1 2936
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
2322 5196.98 5.07452e-005 17.0471 6.092e-006 0.001 3031 LS failed, Hessian reset
2399 5197 0.00161251 5.31807 1 1 3122
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
2441 5197.01 4.59723e-005 5.32003 0.4935 0.4935 3184
Optimization terminated normally:
Convergence detected: relative gradient magnitude is below tolerance
>>> fig = m.plot(forecast)
>>> fig.show()
过拟合时，减小该参数
>>> m = Prophet(changepoint_prior_scale=0.001)
>>> forecast = m.fit(df).predict(future)
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Initial log joint probability = -130.381
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
99 4602.09 3.56568e-006 4040.87 0.712 0.712 130
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
163 4602.61 6.51973e-009 4651.41 0.1156 0.1156 204
Optimization terminated normally:
Convergence detected: absolute parameter change was below tolerance
>>> fig = m.plot(forecast)
>>> fig.show()
3.手动确定变化点位置
使用 changepoints 参数手动指定潜在变化点的位置，只允许在这些点更改斜率，并使用与以前相同的稀疏正则化。自动创建一个点网格，然后用一些已知可能发生变化的特定日期来扩充该网格。 变更点可以完全限制为一小组日期
>>> m = Prophet(changepoints=['2014-01-01'])
>>> forecast = m.fit(df).predict(future)
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Initial log joint probability = -130.381
Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
38 4882.63 1.85072e-005 1.0142 0.3788 1 47
Optimization terminated normally:
Convergence detected: relative gradient magnitude is below tolerance
>>> fig = m.plot(forecast)