# -*- coding: utf-8 -*-
import pandas as pd
from ..compat.numpy import DTYPE
from ._base import fetch_from_web_or_disk
__all__ = [
'load_gasoline'
]
url = 'http://alkaline-ml.com/datasets/gasoline.csv'
[docs]def load_gasoline(as_series=False, dtype=DTYPE):
"""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.
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
See Also
--------
:class:`pmdarima.preprocessing.exog.FourierFeaturizer`
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
References
----------
.. [1] http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=wgfupus2&f=W
.. [2] https://robjhyndman.com/hyndsight/forecasting-weekly-data/
Returns
-------
rslt : array-like, shape=(n_samples,)
The gasoline dataset. There are 745 examples.
""" # noqa
rslt = fetch_from_web_or_disk(url, 'gasoline', cache=True).astype(dtype)
if not as_series:
return rslt
return pd.Series(rslt)