docs: def bootstrap_ts(y, func, B=1000, b=3):
      """ Bootstrap a timeseries using a window size:b. """
      beta_star = np.empty(B)
      z = y
      z_star = np.empty(len(z))
      for boot_i in range(B):
          for block_i, start in enumerate(np.random.randint(len(z) - b + 1, size=len(z) / b)):
              z_star[block_i * b:(block_i + 1) * b] = z[start:start + b]
              beta_star[boot_i] = func(z_star)
      return beta_star