# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
from ..compat import DTYPE
__all__ = [
'load_airpassengers'
]
[docs]def load_airpassengers(as_series=False, dtype=DTYPE):
"""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.
Examples
--------
>>> from pmdarima.datasets import load_airpassengers
>>> load_airpassengers() # doctest: +SKIP
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
Notes
-----
This is monthly data, so *m* should be set to 12 when using in a seasonal
context.
References
----------
.. [1] Box, G. E. P., Jenkins, G. M. and Reinsel, G. C. (1976)
"Time Series Analysis, Forecasting and Control. Third Edition."
Holden-Day. Series G.
"""
rslt = 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
]).astype(dtype)
if as_series:
return pd.Series(rslt)
return rslt