ENH: Add ability to append to a model

arch/univariate/base.py

@@ -35,7 +35,7 @@
 )
 from arch.univariate.distribution import Distribution, Normal
 from arch.univariate.volatility import ConstantVariance, VolatilityProcess
-from arch.utility.array import ensure1d
+from arch.utility.array import ensure1d, append_same_type
 from arch.utility.exceptions import (
     ConvergenceWarning,
     DataScaleWarning,
@@ -230,6 +230,28 @@ def name(self) -> str:
         """The name of the model."""
         return self._name
 
+    def append(self, y: ArrayLike) -> None:
+        """
+        Append data to the model
+
+        Parameters
+        ----------
+        y : ndarray or Series
+            Data to append
+
+        Returns
+        -------
+        ARCHModel
+            Model with data appended
+        """
+        _y = ensure1d(y, "y", series=True)
+        self._y_original = append_same_type(self._y_original, y)
+        self._y_series = pd.concat([self._y_series, _y])
+        self._y = np.concatenate([self._y, np.asarray(_y)])
+
+        self._fit_indices: [0, int(self._y.shape[0])]
+        self._fit_y = self._y
+
     def constraints(self) -> tuple[Float64Array, Float64Array]:
         """
         Construct linear constraint arrays  for use in non-linear optimization

arch/univariate/mean.py

@@ -39,6 +39,7 @@
     SkewStudent,
     StudentsT,
 )
+from arch.utility.array import append_same_type
 
 if TYPE_CHECKING:
     # Fake path to satisfy mypy
@@ -269,6 +270,7 @@ def __init__(
             distribution=distribution,
             rescale=rescale,
         )
+        self._x_original = x
         self._x = x
         self._x_names: list[str] = []
         self._x_index: None | NDArray | pd.Index = None
@@ -307,6 +309,25 @@ def __init__(
 
         self._init_model()
 
+    def append(self, y: ArrayLike, x: ArrayLike2D | None = None) -> None:
+        super().append(y)
+        if x is not None:
+            if self._x is None:
+                raise ValueError("x was not provided in the original model")
+            _x = np.atleast_2d(np.asarray(x))
+            if _x.ndim != 2:
+                raise ValueError("x must be 2-d")
+            elif _x.shape[1] != self._x.shape[1]:
+                raise ValueError(
+                    "x must have the same number of columns as the original x"
+                )
+            self._x_original = append_same_type(self._x_original, x)
+            self._x = np.asarray(self._x_original)
+            if self._x.shape[0] != self._y.shape[0]:
+                raise ValueError("x must have the same number of observations as y")
+
+            self._init_model()
+
     def _scale_changed(self):
         """
         Called when the scale has changed.  This allows the model

arch/utility/array.py

@@ -12,7 +12,16 @@
 from typing import Any, Literal, overload
 
 import numpy as np
-from pandas import DataFrame, DatetimeIndex, Index, NaT, Series, Timestamp, to_datetime
+from pandas import (
+    DataFrame,
+    DatetimeIndex,
+    Index,
+    NaT,
+    Series,
+    Timestamp,
+    to_datetime,
+    concat,
+)
 
 from arch.typing import AnyPandas, ArrayLike, DateLike, NDArray
 
@@ -310,3 +319,23 @@ def find_index(s: AnyPandas, index: int | DateLike) -> int:
     if loc.size == 0:
         raise ValueError("index not found")
     return int(loc)
+
+
+def append_same_type(original, new):
+    if not isinstance(new, type(original)):
+        raise TypeError(
+            "Input data must be the same type as the original data. "
+            f"Got {type(new)}, expected {type(original)}."
+        )
+    if isinstance(original, (Series, DataFrame)):
+        extended = concat([original, new], axis=0)
+    elif isinstance(original, np.ndarray):
+        extended = np.concatenate([original, new])
+    elif isinstance(original, list):
+        extended = original + new
+    else:
+        raise TypeError(
+            "Input data must be a pandas Series, DataFrame, numpy ndarray, or "
+            f"list. Got {type(original)}."
+        )
+    return extended