pmdarima.datasets.load_lynx

pmdarima.datasets.load_lynx(as_series=False, dtype=<class 'numpy.float64'>)[source][source]

Annual numbers of lynx trappings for 1821–1934 in Canada.

This time-series records the number of skins of predators (lynx) that were collected over several years by the Hudson’s Bay Company. The dataset was taken from Brockwell & Davis (1991) and appears to be the series considered by Campbell & Walker (1977).

Parameters:

as_series : bool, optional (default=False)

Whether to return a Pandas series. If True, the index will be set to the observed years. 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:

lynx : array-like, shape=(n_samples,)

The lynx dataset. There are 114 observations.

Notes

This is annual data and not seasonal in nature (i.e., \(m=1\))

References

[R75]

Brockwell, P. J. and Davis, R. A. (1991) Time Series and Forecasting Methods. Second edition. Springer. Series G (page 557).

Examples

>>> from pmdarima.datasets import load_lynx
>>> load_lynx()
array([ 269,  321,  585,  871, 1475, 2821, 3928, 5943, 4950, 2577,  523,
         98,  184,  279,  409, 2285, 2685, 3409, 1824,  409,  151,   45,
         68,  213,  546, 1033, 2129, 2536,  957,  361,  377,  225,  360,
        731, 1638, 2725, 2871, 2119,  684,  299,  236,  245,  552, 1623,
       3311, 6721, 4254,  687,  255,  473,  358,  784, 1594, 1676, 2251,
       1426,  756,  299,  201,  229,  469,  736, 2042, 2811, 4431, 2511,
        389,   73,   39,   49,   59,  188,  377, 1292, 4031, 3495,  587,
        105,  153,  387,  758, 1307, 3465, 6991, 6313, 3794, 1836,  345,
        382,  808, 1388, 2713, 3800, 3091, 2985, 3790,  674,   81,   80,
        108,  229,  399, 1132, 2432, 3574, 2935, 1537,  529,  485,  662,
       1000, 1590, 2657, 3396])

>>> load_lynx(True).head()
1821     269
1822     321
1823     585
1824     871
1825    1475
dtype: int64

Examples using pmdarima.datasets.load_lynx

Fitting an auto_arima model

Fitting an auto_arima model

Adding new observations to your model

Adding new observations to your model

Dataset loading

Dataset loading