load_uschange#

load_uschange(y_name='Consumption')[source]#

Load MTS dataset for forecasting Growth rates of personal consumption and income.

Returns
ypd.Series

selected column, default consumption

Xpd.DataFrame

columns with explanatory variables

Notes

Percentage changes in quarterly personal consumption expenditure, personal disposable income, production, savings and the unemployment rate for the US, 1960 to 2016.

Dimensionality: multivariate Columns: [‘Quarter’, ‘Consumption’, ‘Income’, ‘Production’,

‘Savings’, ‘Unemployment’]

Series length: 188 Frequency: Quarterly Number of cases: 1

This data shows an increasing trend, non-constant (increasing) variance and periodic, seasonal patterns.

References

1

Data for “Forecasting: Principles and Practice” (2nd Edition)

Examples

>>> from sktime.datasets import load_uschange
>>> y, X = load_uschange()