sktime.datasets.base.load_uschange

sktime.datasets.base.load_uschange(y_name='Consumption')[source]

Load the multivariate time series dataset for forecasting Growth rates of personal consumption and personal income.

Returns

  • y (pandas Series) – selected column, default consumption

  • X (pandas Dataframe) – columns with explanatory variables

  • Details

  • ——-

  • 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

Notes

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

References

..fpp2: Data for “Forecasting: Principles and Practice” (2nd Edition)