[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/arima.py

@@ -12,12 +12,13 @@
 array([10.99999999, 12.00000001])
 """
 
-
 import numpy as np
 
 
 class ARIMAModel:
- def __init__(self, ar_order: int = 1, diff_order: int = 0, ma_order: int = 0) -> None:
+ def __init__(
+ self, ar_order: int = 1, diff_order: int = 0, ma_order: int = 0
+ ) -> None:
  """Initialize ARIMA model.
  Args:
  ar_order: Autoregressive order (p)
@@ -51,9 +52,11 @@ def fit(self, time_series: np.ndarray) -> "ARIMAModel":
  y = np.asarray(time_series)
  y_diff = self.difference(y, self.diff_order)
  # Build lagged feature matrix
- feature_matrix = np.column_stack([np.roll(y_diff, i) for i in range(1, self.ar_order + 1)])
- feature_matrix = feature_matrix[self.ar_order:]
- target = y_diff[self.ar_order:]
+ feature_matrix = np.column_stack(
+ [np.roll(y_diff, i) for i in range(1, self.ar_order + 1)]
+ )
+ feature_matrix = feature_matrix[self.ar_order :]
+ target = y_diff[self.ar_order :]
  # Add intercept
  feature_matrix = np.hstack(
  [np.ones((feature_matrix.shape[0], 1)), feature_matrix]
@@ -79,10 +82,10 @@ def predict(self, time_series: np.ndarray, n_periods: int = 1) -> np.ndarray:
  array([10.99999999, 12.00000001])
  """
  y = np.asarray(time_series)
- y_pred = list(y[-self.ar_order:])
+ y_pred = list(y[-self.ar_order :])
  for _ in range(n_periods):
  # Build feature vector for prediction
- features = [1] + y_pred[-self.ar_order:][::-1]
+ features = [1] + y_pred[-self.ar_order :][::-1]
  next_val = np.dot(features, self.coef_)
  y_pred.append(next_val)
- return np.array(y_pred[self.ar_order:])
+ return np.array(y_pred[self.ar_order :])