Nixtla
Synthetic 🧬 Forecast
Fast synthetic time series for testing, augmentation, and pretraining
SynForecast generates synthetic time-series panels with 32 statistical, stochastic, multivariate, domain-specific, and pretraining generators. It follows the Nixtla long format and supports controlled changepoints, anomalies, missing data, exogenous variables, and augmentation of observed series.
Installation
Install SynForecast from PyPI:
pip install synforecast
Prebuilt wheels include the required Rust extension on supported platforms. Building from the source distribution requires a Rust toolchain.
Quick start
Generate a long-format panel from a balanced collection of generators:
from synforecast import generate_series
synthetic_df = generate_series(
n_series=100,
freq="D",
min_length=100,
max_length=100,
seed=42,
)
The result is a pandas DataFrame with the standard Nixtla columns
[unique_id, ds, y]. Pass engine="polars" to return a Polars DataFrame.
Use a specific generator when you need explicit control over the data-generating process:
from synforecast.generators import SeasonalGenerator
generator = SeasonalGenerator(
min_length=365,
max_length=365,
freq="D",
seasonality_period=7,
seasonality_amplitude=10.0,
seed=42,
)
synthetic_df = generator.generate(n_series=20)
See the quick start and generator reference for the complete API.
Augment observed series
SynAugment analyzes each input series, fits a suitable generator, and adds
synthetic series with matching timestamps and statistical characteristics:
from synforecast import SynAugment
augmenter = SynAugment(seed=42)
augmented_train_df = augmenter.augment(train_df, n_augment=2)
For non-parametric augmentation, bootstrap local remainder blocks or construct panel-aware DTW barycenters:
mbb_train_df = augmenter.mbb(
train_df,
n_augment=2,
seasonal_period=24,
)
dba_train_df = augmenter.dba(
train_df,
n_augment=2,
n_neighbors=3,
)
Fit augmentation parameters on the training split only; fitting on validation or test observations would leak information into model training. See the augmentation guide for generator overrides and diagnostics, or the non-parametric augmentation guide for MBB and DBA behavior and constraints.
Works with the Nixtlaverse
SynForecast materializes the same [unique_id, ds, y] schema used throughout
the Nixtlaverse. The generated or augmented DataFrame can be passed directly
to forecasting libraries without a SynForecast-specific adapter.
NeuralForecast
from neuralforecast import NeuralForecast
from neuralforecast.models import NHITS
nf = NeuralForecast(
models=[NHITS(h=14, input_size=28, max_steps=100)],
freq="D",
)
nf.fit(df=synthetic_df)
neural_forecasts = nf.predict()
MLForecast
from mlforecast import MLForecast
from sklearn.linear_model import LinearRegression
mlf = MLForecast(
models=LinearRegression(),
freq="D",
lags=[1, 7, 14],
)
mlf.fit(synthetic_df)
ml_forecasts = mlf.predict(h=14)
StatsForecast
from statsforecast import StatsForecast
from statsforecast.models import AutoETS
sf = StatsForecast(
models=[AutoETS(season_length=7)],
freq="D",
)
sf.fit(synthetic_df)
statistical_forecasts = sf.predict(h=14)
Install the forecasting libraries you want to use separately. SynForecast does not add them to its runtime dependencies. See the Nixtlaverse guide for materialized pretraining and augmentation workflows.
Why SynForecast?
Synthetic time series are useful when real observations are scarce, sensitive, expensive, or do not cover the conditions a system must handle. SynForecast is designed for:
- Testing forecasting and anomaly-detection pipelines against known behavior
- Augmenting small training panels without altering validation data
- Pretraining global forecasting models on diverse temporal processes
- Stress-testing changepoints, missingness, anomalies, and regime changes
- Reproducible simulation with explicit, validated generator configurations
Synthetic data reflects the assumptions of its generators. Validate those
assumptions and downstream performance for your use case. SynForecast is not an
anonymization or differential-privacy tool: SynAugment is fitted to observed
data and its output can resemble that data.
Evidence and benchmarks
SynForecast's native Rust batch path is designed for high-throughput data
generation. Reproducible scripts and committed result summaries are available in
benchmarks/;
performance depends on generator, series shape, thread count, and hardware.
Synthetic data does not improve every model or dataset. The when synthetic data helps guide reports positive, neutral, and negative results so that augmentation and pretraining choices can be evaluated against observed-only baselines.
Features
- 32 generators across statistical, stochastic, multivariate, domain-specific, and pretraining categories
- Long-format output following Nixtla conventions
- pandas, Polars, cuDF, Modin, and PyArrow output through Narwhals
- Changepoint, anomaly, missingness, and exogenous-variable injection
- Dataset composition with
SynSetand augmentation withSynAugment - Fit-and-simulate, TSMixup, moving-block-bootstrap, and DTW-barycenter augmentation strategies
- Seed-deterministic parallel generation
- Native generation through Rust and PyO3
The pandas and Polars engines are covered by the full test suite. cuDF, Modin, and PyArrow support uses Narwhals and is smoke-tested when those optional libraries are installed; install the selected dataframe library separately.
Documentation
- Getting started
- Tutorials and capabilities
- Generator reference
- Contributing
- Roadmap
- Changelog
- Support
- Security policy
- Citation
AI disclaimer
Parts of this project were developed with assistance from generative AI tools. All AI-assisted code and documentation are reviewed, tested, and maintained by human contributors, who remain responsible for correctness, security, licensing, and design.
License
SynForecast is licensed under the Apache License 2.0.
1 maintainer
Metadata
1 maintainer
Release files for synforecast 0.2.0
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