pmdarima.datasets.load_airpassengers

pmdarima.datasets.load_airpassengers(as_series=False, dtype=<class 'numpy.float64'>)

Monthly airline passengers.

The classic Box & Jenkins airline data. Monthly totals of international airline passengers, 1949 to 1960.

Parameters:

as_series : bool, optional (default=False)

Whether to return a Pandas series. 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 time series vector.

Notes

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

References

[R69]

Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976) “Time Series Analysis, Forecasting and Control. Third Edition.” Holden-Day. Series G.

Examples

>>> from pmdarima.datasets import load_airpassengers
>>> load_airpassengers()  
np.array([
    112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118,
    115, 126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140,
    145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166,
    171, 180, 193, 181, 183, 218, 230, 242, 209, 191, 172, 194,
    196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201,
    204, 188, 235, 227, 234, 264, 302, 293, 259, 229, 203, 229,
    242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278,
    284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306,
    315, 301, 356, 348, 355, 422, 465, 467, 404, 347, 305, 336,
    340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337,
    360, 342, 406, 396, 420, 472, 548, 559, 463, 407, 362, 405,
    417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432])

>>> load_airpassengers(True).head()
0    112.0
1    118.0
2    132.0
3    129.0
4    121.0
dtype: float64

Examples using pmdarima.datasets.load_airpassengers

Seasonal decomposition of your time-series

Seasonal decomposition of your time-series

Dataset loading

Dataset loading