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# BaseGenerator
> Base class for all time series generators
### `BaseGenerator`
Bases: [BaseModel](#pydantic.BaseModel), [ABC](#abc.ABC)
Base class for all time series generators.
**Parameters:**
| Name | Type | Description | Default |
| ------------------------------ | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------ | ---------- |
| `min_length` | [int](#int) | Minimum length of each series | *required* |
| `max_length` | [int](#int) | Maximum length of each series | *required* |
| `freq` | [str](#str) \| [int](#int) | Frequency of the data. Either a pandas offset alias (e.g. 'D', 'h', '5min', 'MS', 'W-MON') or an integer for an integer time index | *required* |
| `engine` | [str](#str) | Output dataframe library (default: 'pandas'). Options are 'pandas', 'polars', 'cudf', 'modin', 'pyarrow' | *required* |
| `alias` | [str](#str) \| None | Name of the generator (default: class name) | *required* |
| `id_col` | [str](#str) | Name of the ID column (default: 'unique\_id') | *required* |
| `time_col` | [str](#str) | Name of the timestamp column (default: 'ds') | *required* |
| `target_col` | [str](#str) | Name of the value column (default: 'y') | *required* |
| `start_datetime` | [str](#str) | First timestamp of every series, in any format accepted by pandas.Timestamp (default: '2000-01-01'). Ignored when freq is an integer | *required* |
| `seed` | [int](#int) \| None | Random seed for reproducibility (default: None) | *required* |
| `Exogenous parameters` | | | *required* |
| `exogenous` | [ExogenousConfig](#synforecast.exogenous.ExogenousConfig) \| None | Configuration for exogenous variable generation. None = no exogenous columns (default: None) | *required* |
| `Missingness parameters` | | | *required* |
| `missing_data` | [bool](#bool) | Enable missing data patterns (default: False) | *required* |
| `missing_pattern` | [str](#str) | Pattern: 'random', 'block', 'seasonal' (default: 'random') | *required* |
| `missing_rate` | [float](#float) | Proportion of missing values 0-1 (default: 0.1) | *required* |
| `missing_block_size` | [int](#int) | Size of missing blocks for 'block' pattern (default: 3) | *required* |
| `missing_seasonal_period` | [int](#int) | Period for 'seasonal' pattern (default: 7) | *required* |
| `Anomaly parameters` | | | *required* |
| `anomalies` | [bool](#bool) | Enable anomaly injection (default: False) | *required* |
| `anomaly_fraction` | [float](#float) | Fraction of points that are anomalies (default: 0.05) | *required* |
| `anomaly_types` | [list](#list)\[[str](#str)] | Types: 'spike', 'dip', 'level\_shift' (default: \['spike', 'dip']) | *required* |
| `spike_magnitude` | [float](#float) | Magnitude of spikes (default: 10.0) | *required* |
| `dip_magnitude` | [float](#float) | Magnitude of dips (default: -10.0) | *required* |
| `level_shift_magnitude` | [float](#float) | Magnitude of level shifts (default: 20.0) | *required* |
| `level_shift_duration` | [int](#int) | Duration of level shifts in time steps (default: 10) | *required* |
| `Changepoint parameters` | | | *required* |
| `changepoints` | [bool](#bool) | Enable changepoint injection (default: False) | *required* |
| `num_changepoints` | [int](#int) | Number of changepoints (default: 2) | *required* |
| `changepoint_type` | [str](#str) | Type: 'level', 'trend', 'variance', 'mixed' (default: 'level') | *required* |
| `changepoint_level_changes` | [list](#list)\[[float](#float)] \| None | Size of level changes (default: random) | *required* |
| `changepoint_trend_changes` | [list](#list)\[[float](#float)] \| None | Size of trend changes (default: random) | *required* |
| `changepoint_variance_changes` | [list](#list)\[[float](#float)] \| None | Size of variance changes (default: random) | *required* |
| `changepoint_locations` | [list](#list)\[[float](#float)] \| None | Relative positions 0-1 (default: random) | *required* |
#### `BaseGenerator.generate`
```python theme={null}
generate(n_series, start_id=0, n_jobs=-1)
```
Generate synthetic time series data.
**Parameters:**
| Name | Type | Description | Default |
| ---------- | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------- |
| `n_series` | [int](#int) | Number of time series to generate | *required* |
| `start_id` | [int](#int) | Starting ID for the series numbering (default: 0) Series will be numbered from start\_id to start\_id + n\_series - 1 | 0 |
| `n_jobs` | [int](#int) | Number of parallel workers. -1 (default) uses `RAYON_NUM_THREADS` if set, otherwise all logical cores. Results are seed-deterministic and do not depend on n\_jobs. | -1 |
**Returns:**
| Type | Description |
| ------------------------------------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------- |
| [IntoDataFrameT](#narwhals.stable.v2.typing.IntoDataFrameT) | DataFrame in long format with columns \[id\_col, time\_col, target\_col] (default \['unique\_id', 'ds', 'y']), plus any exogenous or flag columns. |
#### `BaseGenerator.generate_single_series`
```python theme={null}
generate_single_series(length)
```
Generate values for a single time series.
**Parameters:**
| Name | Type | Description | Default |
| -------- | ------------------------ | ------------------------------------ | ---------- |
| `length` | [int](#int) | The length of the series to generate | *required* |
**Returns:**
| Type | Description |
| -------------------------------------- | --------------------------- |
| [ndarray](#numpy.ndarray) | Array of time series values |