pmdarima.datasets.load_gasoline

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

Weekly US finished motor gasoline products

A weekly time series of US finished motor gasoline products supplied (in thousands of barrels per day) from February 1991 to May 2005.

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 gasoline dataset. There are 745 examples.

See also

pmdarima.preprocessing.exog.FourierFeaturizer

Notes

The seasonal periodicity of this example is rather difficult, since it’s not an integer. To be exact, the periodicity is 365.25 / 7 (~=52.1785714285714). To fit the best possible model to this data, you’ll need to explore using exogenous features

References

[R77]http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=wgfupus2&f=W
[R78]https://robjhyndman.com/hyndsight/forecasting-weekly-data/

Examples

>>> from pmdarima.datasets import load_gasoline
>>> load_gasoline()
array([6621. , 6433. , 6582. , ..., 9024. , 9175. , 9269. ])

>>> load_gasoline(True).head()
0    6621.0
1    6433.0
2    6582.0
3    7224.0
4    6875.0
dtype: float64