generate_series
generate_series(n_series, freq='D', min_length=50, max_length=500, n_static_features=0, equal_ends=False, with_trend=False, static_as_categorical=True, n_models=0, level=None, engine='pandas', seed=0, n_hist_exog=0, n_futr_exog=0, h=0)
| Name | Type | Description | Default |
|---|---|---|---|
n_series | int | Number of series for synthetic panel. | required |
freq | str | Frequency of the data (pandas alias). Seasonalities are implemented for hourly, daily and monthly. Defaults to ‘D’. | ‘D’ |
min_length | int | Minimum length of synthetic panel’s series. Defaults to 50. | 50 |
max_length | int | Maximum length of synthetic panel’s series. Defaults to 500. | 500 |
n_static_features | int | Number of static exogenous variables for synthetic panel’s series. Defaults to 0. | 0 |
equal_ends | bool | Series should end in the same timestamp. Defaults to False. | False |
with_trend | bool | Series should have a (positive) trend. Defaults to False. | False |
static_as_categorical | bool | Static features should have a categorical data type. Defaults to True. | True |
n_hist_exog | int | Number of historic exogenous variables. Must be non-negative. Creates columns named hist_exog_{i} only in the historic dataframe. Defaults to 0. | 0 |
n_futr_exog | int | Number of future exogenous variables. Must be non-negative. Each variable is generated once and split between the historic and future dataframes. Creates columns named futr_exog_{i} in both dataframes. Defaults to 0. | 0 |
h | int | Number of future periods to generate per series when n_futr_exog is greater than 0. Defaults to 0. | 0 |
n_models | int | Number of models predictions to simulate. Defaults to 0. | 0 |
level | list of float | Confidence level for intervals to simulate for each model. Defaults to None. | None |
engine | str | Output Dataframe type. Defaults to ‘pandas’. | ‘pandas’ |
seed | int | Random seed used for generating the data. Defaults to 0. | 0 |