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.


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.


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

The gasoline dataset. There are 745 examples.

See also



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




>>> 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