API Reference
Data
Utilies for generating time series datasets
source
generate_series
generate_series (n_series:int, freq:str='D', min_length:int=50, max_length:int=500, n_static_features:int=0, equal_ends:bool=False, with_trend:bool=False, static_as_categorical:bool=True, n_models:int=0, level:Optional[List[float]]=None, engine:Literal['pandas','polars']='pandas', seed:int=0)
Generate Synthetic Panel Series.
Type | Default | Details | |
---|---|---|---|
n_series | int | Number of series for synthetic panel. | |
freq | str | D | Frequency of the data (pandas alias). Seasonalities are implemented for ‘H’, ‘D’ and ‘M’. |
min_length | int | 50 | Minimum length of synthetic panel’s series. |
max_length | int | 500 | Maximum length of synthetic panel’s series. |
n_static_features | int | 0 | Number of static exogenous variables for synthetic panel’s series. |
equal_ends | bool | False | Series should end in the same date stamp ds . |
with_trend | bool | False | Series should have a (positive) trend. |
static_as_categorical | bool | True | Static features should have a categorical data type. |
n_models | int | 0 | Number of models predictions to simulate. |
level | Optional | None | Confidence level for intervals to simulate for each model. |
engine | Literal | pandas | Output Dataframe type. |
seed | int | 0 | Random seed used for generating the data. |
Returns | Union | Synthetic panel with columns [unique_id , ds , y ] and exogenous features. |
synthetic_panel = generate_series(n_series=2)
synthetic_panel.groupby('unique_id', observed=True).head(4)
unique_id | ds | y | |
---|---|---|---|
0 | 0 | 2000-01-01 | 0.357595 |
1 | 0 | 2000-01-02 | 1.301382 |
2 | 0 | 2000-01-03 | 2.272442 |
3 | 0 | 2000-01-04 | 3.211827 |
222 | 1 | 2000-01-01 | 5.399023 |
223 | 1 | 2000-01-02 | 6.092818 |
224 | 1 | 2000-01-03 | 0.476396 |
225 | 1 | 2000-01-04 | 1.343744 |