reconnect moved files to git repo

2025-08-01 04:33:03 -04:00
commit 5d3c35492d
23190 changed files with 4750716 additions and 0 deletions
--- a/venv/lib/python3.11/site-packages/pmdarima/datasets/austres.py
+++ b/venv/lib/python3.11/site-packages/pmdarima/datasets/austres.py
@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+
+import numpy as np
+import pandas as pd
+
+from ..compat import DTYPE
+
+__all__ = [
+    'load_austres'
+]
+
+
+def load_austres(as_series=False, dtype=DTYPE):
+    """Quarterly residential data.
+
+    Numbers (in thousands) of Australian residents measured quarterly from
+    March 1971 to March 1994.
+
+    Parameters
+    ----------
+    as_series : bool, optional (default=False)
+        Whether to return a Pandas series. If False, will return a 1d
+        numpy array.
+
+    dtype : type, optional (default=np.float64)
+        The type to return for the array. Default is np.float64, which is used
+        throughout the package as the default type.
+
+    Returns
+    -------
+    rslt : array-like, shape=(n_samples,)
+        The austres vector.
+
+    Examples
+    --------
+    >>> from pmdarima.datasets import load_austres
+    >>> load_austres()
+    np.array([13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9,
+              13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5,
+              13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8,
+              13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6,
+              14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6,
+              14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4,
+              14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7,
+              15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2,
+              15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5,
+              15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5,
+              16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9,
+              16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2,
+              16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9,
+              17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3,
+              17482.6, 17526.0, 17568.7, 17627.1, 17661.5])
+
+    >>> load_austres(True).head()
+    0    13067.3
+    1    13130.5
+    2    13198.4
+    3    13254.2
+    4    13303.7
+    dtype: float64
+
+    Notes
+    -----
+    This is quarterly data, so *m* should be set to 4 when using in a seasonal
+    context.
+
+    References
+    ----------
+    .. [1] P. J. Brockwell and R. A. Davis (1996)
+           "Introduction to Time Series and Forecasting." Springer
+    """
+    rslt = np.array([
+        13067.3, 13130.5, 13198.4, 13254.2, 13303.7, 13353.9,
+        13409.3, 13459.2, 13504.5, 13552.6, 13614.3, 13669.5,
+        13722.6, 13772.1, 13832.0, 13862.6, 13893.0, 13926.8,
+        13968.9, 14004.7, 14033.1, 14066.0, 14110.1, 14155.6,
+        14192.2, 14231.7, 14281.5, 14330.3, 14359.3, 14396.6,
+        14430.8, 14478.4, 14515.7, 14554.9, 14602.5, 14646.4,
+        14695.4, 14746.6, 14807.4, 14874.4, 14923.3, 14988.7,
+        15054.1, 15121.7, 15184.2, 15239.3, 15288.9, 15346.2,
+        15393.5, 15439.0, 15483.5, 15531.5, 15579.4, 15628.5,
+        15677.3, 15736.7, 15788.3, 15839.7, 15900.6, 15961.5,
+        16018.3, 16076.9, 16139.0, 16203.0, 16263.3, 16327.9,
+        16398.9, 16478.3, 16538.2, 16621.6, 16697.0, 16777.2,
+        16833.1, 16891.6, 16956.8, 17026.3, 17085.4, 17106.9,
+        17169.4, 17239.4, 17292.0, 17354.2, 17414.2, 17447.3,
+        17482.6, 17526.0, 17568.7, 17627.1, 17661.5]).astype(dtype)
+
+    if as_series:
+        return pd.Series(rslt)
+    return rslt