Time-Series-Correlation

import numpy as np
from scipy.stats import kendalltau


def kendall_tau_with_n_lags(x, y, n_lags):
  """Computes the Kendall tau correlation coefficient between two time series with n lags.

  Args:
    x: A NumPy array containing the first time series.
    y: A NumPy array containing the second time series.
    n_lags: An integer representing the number of lags to test.

  Returns:
    A NumPy array containing the Kendall tau correlation coefficients for each lag.
  """

  kendall_tau_correlations = np.zeros(n_lags)
  kendall_tau_p_values = np.zeros(n_lags)

  for lag in range(n_lags):
    kendall_tau_correlation, p_value = kendalltau(x[lag:], y[:-lag])
    kendall_tau_correlations[lag] = kendall_tau_correlation
    kendall_tau_p_values[lag] = p_value

  return kendall_tau_correlations, kendall_tau_p_values


def print_kendall_tau_with_n_lags(x, y, n_lags):
  """Prints the Kendall tau correlation coefficient between two time series with n lags, with p-value added.

  Args:
    x: A NumPy array containing the first time series.
    y: A NumPy array containing the second time series.
    n_lags: An integer representing the number of lags to test.
  """

  kendall_tau_correlations, kendall_tau_p_values = kendall_tau_with_n_lags(x, y, n_lags)

  print("Kendall tau correlation coefficients with n lags:")
  for lag, kendall_tau_correlation, kendall_tau_p_value in zip(range(n_lags), kendall_tau_correlations, kendall_tau_p_values):
    print(f"Lag {lag}: {kendall_tau_correlation:.4f}, p-value: {kendall_tau_p_value:.4f}")


if __name__ == "__main__":
  x = np.array([1, 2, 3, 4, 5])
  y = np.array([2, 4, 6, 8, 10])
  n_lags = 3

  print_kendall_tau_with_n_lags(x, y, n_lags)