> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Works with the Nixtlaverse

> Compare observed, augmented, and synthetic-only training workflows.

SynForecast produces the standard Nixtla long format: `unique_id`
identifies a series, `ds` identifies time, and `y` contains the target.
Materialized SynForecast data can therefore be passed directly to
NeuralForecast, MLForecast, or StatsForecast.

The executable guides compare three training regimes against the same
temporal holdout:

* **Observed only:** the baseline that synthetic workflows must beat.
* **Observed + synthetic:** `SynAugment` is fitted to the training
  split and adds statistically matched histories.
* **Synthetic only:** the forecasting model is fitted without observed
  target values. The exact transfer mechanism depends on the model
  family.

## Integration guides

* [NeuralForecast](neuralforecast.ipynb): zero-shot synthetic
  pretraining and synthetic pretraining followed by fine-tuning.
* [MLForecast](mlforecast.ipynb): global training with augmentation
  and zero-shot transfer through `predict(new_df=...)`.
* [StatsForecast](statsforecast.ipynb): local-model behavior and a
  synthetic-history ensemble instead of transferable pretraining.

All three use the classic Box–Jenkins airline passenger series, reserve
its last 12 months before generation, report holdout MAE, and save their
forecast plots. They are workflow demonstrations rather than evidence
that synthetic data always improves accuracy.

## Materialize synthetic data

```python theme={null}
from synforecast import SynAugment, generate_series

augmented_train_df = SynAugment(seed=42).augment(
    train_df,
    n_augment=2,
)
synthetic_pretrain_df = generate_series(
    n_series=1_000,
    freq="D",
    min_length=100,
    max_length=100,
    seed=42,
)
```

Fit augmentation parameters on training observations only. Using
validation or test values would leak future information into the
generated training set. Synthetic data also inherits its generators’
assumptions, so retain an observed-only baseline and evaluate multiple
temporal folds and random seeds.

These examples materialize data before model fitting. On-the-fly
training-data generation would require a streaming data-source interface
in the forecasting library and is not part of the current integration.