Calculates the transfer entropy from time series X to Y, measuring the reduction in uncertainty about Y’s future when conditioning on X’s past, beyond what Y’s own history provides. Transfer entropy is asymmetric and detects directional information flow, making it ideal for causality detection in financial time series.
Use Cases:
Formula: TE(X→Y) = I(Y_future; X_past | Y_past)
Credits: 5 credits per request (Pro Tier)
API key for authentication. Get your key at https://finceptbackend.share.zrok.io/auth/register
Historical time series for variable X (source)
[
0.12,
0.15,
0.11,
0.18,
0.14,
0.16,
0.13,
0.17,
0.15,
0.19
]Historical time series for variable Y (target)
[
0.1,
0.13,
0.09,
0.16,
0.12,
0.14,
0.11,
0.15,
0.13,
0.17
]Time lag for transfer entropy calculation (number of steps)
1
Number of bins for discretization (higher = finer resolution but needs more data)
10