> ## 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.

# Non-parametric augmentation

> Augment observed panels with moving-block bootstrap or DTW barycenters.

`SynAugment.mbb` and `SynAugment.dba` transform observed series
directly. They do not fit a parametric generator and deliberately bypass
the mean, standard deviation, and lag-1 autocorrelation pinning used by
`SynAugment.augment`.

Use only training observations as input. Augmenting from validation or
test values leaks future information into model training.

## Moving-block bootstrap

MBB separates a series into classical moving-average trend, seasonal,
and remainder components. It resamples overlapping remainder blocks and
combines them with the original trend and seasonal components.

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

augmenter = SynAugment(seed=42)
mbb_df = augmenter.mbb(
    train_df,
    n_augment=2,
    seasonal_period=24,
    block_size=12,
    include_original=True,
)
```

When `seasonal_period=None`, SynForecast attempts to detect a period.
When `block_size=None`, it uses twice the detected period, or
approximately the square root of the series length without seasonality.
The effective block size is capped at `max(2, len(series) // 3)` so
multiple blocks contribute even for short inputs.

MBB is a good fit when local residual dependence matters and the
observed trend and seasonal shape should remain anchored. Each input
series needs at least four observations and one finite value. Missing
values are interpolated before decomposition.

## DTW barycenter averaging

DBA creates a synthetic series around each usable reference series. It
finds nearest neighbours with banded dynamic time warping (DTW),
randomizes their weights, and iteratively computes a weighted
barycenter.

```python theme={null}
dba_df = augmenter.dba(
    train_df,
    n_augment=2,
    n_neighbors=3,
    n_iterations=5,
    window_fraction=0.1,
    scale="reference",
    include_original=True,
)
```

With `scale="reference"`, series are z-normalized for alignment and each
barycenter is mapped back to its reference mean and standard deviation.
Use `scale="none"` only when the panel already shares a meaningful
common scale. The output length and timestamps follow the reference
series, including when its neighbours have different lengths.

DBA needs at least two usable series. DTW time and memory grow with
series length times band width, so reduce `window_fraction`,
`n_neighbors`, or `n_iterations` for long panels. Pairwise panel
distances are computed once per call in bounded parallel chunks in
native Rust. Only the requested nearest neighbors are retained, using
O(number of series × `n_neighbors`) storage; distance computation still
examines every pair. Series without a finite observation are skipped
with a logged warning; `mbb` also skips series shorter than four
observations.

MBB computes its moving-average trend in linear time and reuses each
source’s decomposition across all requested copies.

## Output contract

Both methods:

* return the same dataframe backend as the input unless `engine` is
  set;
* copy all non-target columns from the source/reference series
  unchanged;
* generate collision-safe string IDs such as `store_1_mbb_0` or
  `store_1_dba_0`;
* are deterministic for a fixed `SynAugment` seed; and
* run decomposition, block sampling, DTW, and barycenter updates in
  native Rust.

`include_original=False` returns only synthetic rows. Always compare
augmented training against an observed-only baseline over temporal
holdouts; neither method guarantees better forecast accuracy.

See [SynAugment: data augmentation](./augmentation.html) for
fit-and-simulate and TSMixup workflows, and the [API
reference](../../dataset.html) for every parameter.


## Related topics

- [Synthetic 🧬 Forecast](/synforecast/index.html.md)
- [Simulation Paths | NeuralForecast](/neuralforecast/docs/tutorials/simulation.html.md)
- [PyTorch Losses](/neuralforecast/losses.pytorch.html.md)
- [Data augmentation with real datasets](/synforecast/docs/capabilities/augmentation_real_data.html.md)
- [Dataset](/synforecast/dataset.html.md)