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  name: python3
---

(transfer_entropy)=

# Transfer Entropy

Transfer Entropy (TE) measures the amount of directed transfer of information between
two random processes.

```python
import delaynet as dn

# Calculate transfer entropy
result = dn.connectivity(
    ts1, ts2, metric="transfer_entropy", approach="discrete", lag_steps=5
)
```

or more involved with ordinal transfer entropy

```python
import delaynet as dn

# Calculate transfer entropy
result = dn.connectivity(
    ts1,
    ts2,
    metric="transfer_entropy",
    approach='ordinal',
    lag_steps=5,
    embedding_dim=3
)
```

Parameters:

- `approach`: The approach to use for estimating transfer entropy.
  For available approaches, find the string in {func}`infomeasure.transfer_entropy`.
- `lag_steps`: Time lags to consider. An integer will consider lags [1, ..., lag_steps].
  Passing a list will consider the specified values as lags.
- `hypothesis_type`: Type of hypothesis test to use ('permutation_test' or 'bootstrap').
  Default is 'permutation_test'.
- `n_tests`: Number of iterations or resamples to perform within the hypothesis test.
  Default is 20.
- Additional keyword arguments are passed directly to the transfer entropy estimator.

```{eval-rst}
.. automethod:: delaynet.connectivities.transfer_entropy
    :noindex:
```