co2_by_month = pd.read_csv("monthly_mauna_loa_co2.csv")
co2_by_month["date_month"] = pd.to_datetime(co2_by_month["date_month"])
co2_by_month["CO2"] = co2_by_month["CO2"].astype(np.float32)
co2_by_month.set_index("date_month", drop=True, inplace=True)
num_forecast_steps = 12 * 10
co2_by_month_training_data = co2_by_month[:-num_forecast_steps]
co2_by_month_testing_data = co2_by_month[-num_forecast_steps:]Causal Impact
pip install tf_keras
pip install git+https://github.com/google/tfp-causalimpact.gitimport pandas as pd
np.random.seed(42)
n = 120
time = np.arange(n)
trend = 0.1 * time
seasonal = 10 * np.sin(2 * np.pi * time / 12)
noise = np.random.normal(0, 3, n)
y = 50 + trend + seasonal + noise
event_start = 80
y[event_start:] -= 20
data = pd.DataFrame({'y': y})
data['y'].plot()
plt.savefig('tser_023_bsts_01.jpg')
[devam edecek]
Kaynaklar