pmdarima.datasets.load_austres

pmdarima.datasets.load_austres(as_series=False, dtype=<class 'numpy.float64'>)

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.

Notes

This is quarterly data, so m should be set to 4 when using in a seasonal context.

References

[R73]

P. J. Brockwell and R. A. Davis (1996) “Introduction to Time Series and Forecasting.” Springer

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

Examples using pmdarima.datasets.load_austres

Dataset loading