Continuous Ordinal Patterns

Continuous Ordinal Patterns#

The Continuous Ordinal Patterns (COP) connectivity metric analyzes the patterns in time series data to detect relationships.

import delaynet as dn

# Calculate COP connectivity
result = dn.connectivity(
    ts1, ts2, metric="random_patterns", p_size=5, num_rnd_patterns=50, lag_steps=5
)

Parameters:

  • p_size: Size of the ordinal pattern.

  • num_rnd_patterns: Number of random patterns to consider.

  • lag_steps: Time lags to consider. An integer will consider lags [1, …, lag_steps]. Passing a list will consider the specified values as lags.

  • linear: Whether to start with the identity pattern. Default is True.

continuous_ordinal_patterns.random_patterns(ts2, p_size=5, num_rnd_patterns=50, linear=True, lag_steps: int | list = None)

Continuous Ordinal Patterns (COP) connectivity metric [OMarinRodriguezAZ25, Zan23].

Parameters:

  • ts1 (numpy.ndarray) – First time series.

  • ts2 (numpy.ndarray) – Second time series.

  • p_size (int) – Size of the ordinal pattern.

  • num_rnd_patterns (int) – Number of random patterns to consider.

  • linear (bool) – Start with the identity pattern.

  • lag_steps (int | list) – Time lags to consider. Can be a single integer or a list of integers. An integer will consider lags [1, …, lag_steps]. A list will consider the specified values as lags.

Returns:

Best p-value and corresponding lag.

Return type:

tuple[float, int]