`pmdarima.datasets`.load_wineind

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

Australian total wine sales by wine makers in bottles <= 1 litre.

This time-series records wine sales by Australian wine makers between Jan 1980 – Aug 1994. This dataset is found in the R forecast package.

Parameters:

as_series : bool, optional (default=False)

Whether to return a Pandas series. If True, the index will be set to the observed years/months. 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 wineind dataset. There are 176 observations.

Notes

This is monthly data, so m should be set to 12 when using in a seasonal context.

References

[R81]

https://www.rdocumentation.org/packages/forecast/versions/8.1/topics/wineind

Examples

>>> from pmdarima.datasets import load_wineind
>>> load_wineind()
array([15136, 16733, 20016, 17708, 18019, 19227, 22893, 23739, 21133,
       22591, 26786, 29740, 15028, 17977, 20008, 21354, 19498, 22125,
       25817, 28779, 20960, 22254, 27392, 29945, 16933, 17892, 20533,
       23569, 22417, 22084, 26580, 27454, 24081, 23451, 28991, 31386,
       16896, 20045, 23471, 21747, 25621, 23859, 25500, 30998, 24475,
       23145, 29701, 34365, 17556, 22077, 25702, 22214, 26886, 23191,
       27831, 35406, 23195, 25110, 30009, 36242, 18450, 21845, 26488,
       22394, 28057, 25451, 24872, 33424, 24052, 28449, 33533, 37351,
       19969, 21701, 26249, 24493, 24603, 26485, 30723, 34569, 26689,
       26157, 32064, 38870, 21337, 19419, 23166, 28286, 24570, 24001,
       33151, 24878, 26804, 28967, 33311, 40226, 20504, 23060, 23562,
       27562, 23940, 24584, 34303, 25517, 23494, 29095, 32903, 34379,
       16991, 21109, 23740, 25552, 21752, 20294, 29009, 25500, 24166,
       26960, 31222, 38641, 14672, 17543, 25453, 32683, 22449, 22316,
       27595, 25451, 25421, 25288, 32568, 35110, 16052, 22146, 21198,
       19543, 22084, 23816, 29961, 26773, 26635, 26972, 30207, 38687,
       16974, 21697, 24179, 23757, 25013, 24019, 30345, 24488, 25156,
       25650, 30923, 37240, 17466, 19463, 24352, 26805, 25236, 24735,
       29356, 31234, 22724, 28496, 32857, 37198, 13652, 22784, 23565,
       26323, 23779, 27549, 29660, 23356])

>>> load_wineind(True).head()
Jan 1980    15136
Feb 1980    16733
Mar 1980    20016
Apr 1980    17708
May 1980    18019
dtype: int64

Examples using `pmdarima.datasets.load_wineind`

Simple auto_arima model

Pipelines with auto_arima

Persisting an ARIMA model

Dataset loading

Cross-validation predictions

Cross-validating your time series models

pmdarima.datasets.load_wineind

Examples using pmdarima.datasets.load_wineind

`pmdarima.datasets`.load_wineind

Examples using `pmdarima.datasets.load_wineind`