Comments (3)
@Jamiedee90 it seems to me you already read that file. Do you mind to tel us how is this related to causalimpact
?
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Hi @rpanai
apologies, this was just a dummy line of code. this is to replace the lines:
_np.random.seed(1)
x1 = arma_generate_sample(ar=[0.999], ma=[0.9], nsample=100) + 100
y = 1.2 * x1 + np.random.randn(100)
y[71:100] = y[71:100] + 10
data = pd.DataFrame(np.array([y, x1]).T, columns=["y","x1"])_
This makes it easier to update the data and re use the test across different clients
from causalimpact.
You can save it wherever you want as long as you reference the path in pd.read_csv. In your example traffic.csv would be located in the same directory you are running the code from.
from causalimpact.
Related Issues (20)
- support bayesian estimation methods HOT 3
- does not work with latest version of pandas HOT 1
- How to get P-value in Python version? HOT 7
- Key Error: 'upper y' HOT 41
- compile_posterior_inferences() missing 1 required positional argument: 'estimation' HOT 4
- ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all(). HOT 1
- Apache License? HOT 1
- Think p-value was not calculated properly
- AttributeError: 'CausalImpact' object has no attribute 'inferences' HOT 3
- Binder fails to build HOT 1
- Coverage file is not being created in gh action HOT 1
- Confidence intervals wider than R version
- TypeError: is_list_like() takes 1 positional argument but 2 were given
- Validation and comparison against the R package
- ImportError: cannot import name 'PandasError' HOT 2
- Error in the index attribute HOT 2
- Very different Results R vs Python (and minor Problems) HOT 3
- x axis is displaying incorrectly when modelling series with datetime index HOT 1
- compile_posterior_inferences throws error on integer referencing with a datetime value
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