Update ARIMA time series Model

machine_learning/arima.py

@@ -5,7 +5,7 @@
 
 >>> import numpy as np
 >>> series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
->>> model = ARIMAModel(p=2, d=1, q=0)
+>>> model = ARIMAModel(ar_order=2, diff_order=1, ma_order=0)
 >>> model.fit(series)
 ARIMAModel(...)
 >>> model.predict(series, n_periods=2)
@@ -17,24 +17,24 @@
 
 
 class ARIMAModel:
- def __init__(self, p: int = 1, d: int = 0, q: int = 0) -> None:
+ def __init__(self, ar_order: int = 1, diff_order: int = 0, ma_order: int = 0) -> None:
  """Initialize ARIMA model.
  Args:
- p: AR order
- d: Differencing order
- q: MA order (not used in this implementation)
+ ar_order: Autoregressive order (p)
+ diff_order: Differencing order (d)
+ ma_order: Moving average order (q, not used in this implementation)
  """
- self.p = p
- self.d = d
- self.q = q
+ self.ar_order = ar_order
+ self.diff_order = diff_order
+ self.ma_order = ma_order
  self.coef_: Optional[np.ndarray] = None
  self.resid_: Optional[np.ndarray] = None
 
- def difference(self, series: np.ndarray, order: int) -> np.ndarray:
+ def difference(self, time_series: np.ndarray, order: int) -> np.ndarray:
  """Apply differencing to make series stationary."""
  for _ in range(order):
- series = np.diff(series)
- return series
+ time_series = np.diff(time_series)
+ return time_series
 
  def fit(self, time_series: np.ndarray) -> "ARIMAModel":
  """Fit ARIMA model to the given time series.
@@ -44,18 +44,16 @@ def fit(self, time_series: np.ndarray) -> "ARIMAModel":
  self
  >>> import numpy as np
  >>> series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
- >>> model = ARIMAModel(p=2, d=1, q=0)
+ >>> model = ARIMAModel(ar_order=2, diff_order=1, ma_order=0)
  >>> model.fit(series)
  ARIMAModel(...)
  """
  y = np.asarray(time_series)
- y_diff = self.difference(y, self.d)
+ 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.p + 1)]
- )
- feature_matrix = feature_matrix[self.p :]
- target = y_diff[self.p :]
+ 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]
@@ -74,17 +72,17 @@ def predict(self, time_series: np.ndarray, n_periods: int = 1) -> np.ndarray:
  1D numpy array of forecasted values
  >>> import numpy as np
  >>> series = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
- >>> model = ARIMAModel(p=2, d=1, q=0)
+ >>> model = ARIMAModel(ar_order=2, diff_order=1, ma_order=0)
  >>> model.fit(series)
  ARIMAModel(...)
  >>> model.predict(series, n_periods=2)
  array([10.99999999, 12.00000001])
  """
  y = np.asarray(time_series)
- y_pred = list(y[-self.p :])
+ y_pred = list(y[-self.ar_order:])
  for _ in range(n_periods):
  # Build feature vector for prediction
- features = [1] + y_pred[-self.p :][::-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.p :])
+ return np.array(y_pred[self.ar_order:])