Conformal Impact
Take Causal Impact and replace the Bayesian Structural Time Series Model with MFLES and the Basyesian posterior with Conformal Prediction Intervals.
Quick Examnple an comparison to Causal Impact
intervention_effect = 400
np.random.seed(42)
series = np.random.random((130, 1)) * 400
x_series = series * .4 + np.random.random((130, 1)) * 50 + 1000
trend = (np.arange(1, 131)).reshape((-1, 1))
series += 10 * trend
series[-30:] = series[-30:] + intervention_effect
data = pd.DataFrame(np.column_stack([series, x_series]), columns=['y', 'x1'])
import matplotlib.pyplot as plt
plt.plot(series)
plt.plot(x_series)
plt.show()
from ConformalImpact.Model import CI
conformal_impact = CI(opt_size=20,
opt_steps=10,
opt_step_size=3)
impact_df = conformal_impact.fit(data,
n_windows=30,
intervention_index=100,
seasonal_period=None)
conformal_impact.summary()
conformal_impact.plot()
from causalimpact import CausalImpact
impact = CausalImpact(data, [0, 99], [100, 130])
impact.run()
impact.plot()
print(impact.summary())
output = impact.inferences
np.mean(output['point_effect'].values[-30:])
