Generate bootstrapped samples from time-series data. The full documentation is available here.

1. 🚀 Getting Started
2. 🧩 Modules
3. 🗺 Roadmap
4. 🤝 Contributing
5. 📄 License
6. 📍 Time Series Bootstrapping Methods intro
7. 👏 Contributors

tsbootstrap exposes one typed entry point, bootstrap, configured with a method specification. The same call works for every method.

import numpy as np
from tsbootstrap import bootstrap, MovingBlock

x = np.random.default_rng(0).standard_normal(200)

result = bootstrap(x, method=MovingBlock(block_length="auto"), n_bootstraps=999, random_state=0)

samples = result.values()      # (n_bootstraps, n) resampled series
oob = result.get_oob_mask()    # (n_bootstraps, n) out-of-bag mask

Choose a method spec for the structure you need (block lengths default to the automatic Politis-White selection):

from tsbootstrap import StationaryBlock, ResidualBootstrap, SieveAR, AR, ARIMA, diagnose

bootstrap(x, method=StationaryBlock(avg_block_length="auto"))

# recursive model-based bootstraps (need the model extra: uv add "tsbootstrap[models]")
bootstrap(x, method=ResidualBootstrap(model=AR(order=2)))
bootstrap(x, method=ResidualBootstrap(model=ARIMA(order=(1, 1, 1))))
bootstrap(x, method=SieveAR())

# not sure which fits? ask:
print(diagnose(x).recommended_methods)

Inputs can be NumPy arrays, lists, or pandas / Polars DataFrames and Series. The result is a BootstrapResult carrying the samples, provenance metadata, and out-of-bag / in-bag primitives. For the sktime ecosystem, the same methods are also available as estimator classes (MovingBlockBootstrap, ARResidualBootstrap, SieveBootstrap, and the rest) under tsbootstrap.adapters.

The uq layer turns resampled series into prediction intervals. forecast_intervals gives forward forecast bands for an AR model; EnbPIEnsemble produces out-of-bag prediction intervals for an sklearn-style regressor, with calibrators for stationary, volatility-clustered, and drifting data (static, sliding window, and the adaptive ACI and NexCP schemes); and bootstrap_reduce streams a per-replicate statistic so calibration scales to large replicate counts without holding every path in memory.

from tsbootstrap import AR, forecast_intervals

lower, upper, median = forecast_intervals(x, model=AR(order=2), horizon=12, alpha=0.1)

The conformal pieces (EnbPIEnsemble and the calibrators) need the uq extra (scikit-learn). The interactive tutorial gallery works through every method on real and synthetic data, including a "which bootstrap should I use?" decision guide.

tsbootstrap ships a read-only Model Context Protocol server so an MCP client (an LLM agent, an IDE) can diagnose a short series and compute a bootstrap confidence interval without writing any Python. Run it with no install step:

uvx --from "tsbootstrap[mcp]" tsbootstrap-mcp

It speaks the stdio transport and exposes exactly two read-only tools:

• diagnose_series: serial-dependence and stationarity diagnostics, a recommended Politis-White block length, and the bootstrap methods the server supports for the series.
• bootstrap_confidence_interval: a percentile confidence interval for the mean, median, std, or variance, using an i.i.d. or block bootstrap.

Both tools accept at most 500 observations and run at most 500 replicates. For larger series, model-based methods, or the uncertainty layer, use the library directly in a local script.

Requires Python 3.10 or higher.

# with uv (recommended):
uv add tsbootstrap                   # core: i.i.d. and block methods
uv add "tsbootstrap[models]"         # adds AR / ARIMA / VAR / sieve (statsmodels)

# with pip:
pip install tsbootstrap
pip install "tsbootstrap[models]"

The model-based methods import statsmodels lazily and raise a clear install hint if the models extra is missing.

Package layout:

The full, living roadmap is issue #181. Highlights:

Near term (v0.2.x):

• Out-of-sample forecast intervals for ARIMA and VAR (v0.2.0 ships AR-only).
• The tutorial gallery and getting-started notebook (#46).
• Python 3.14, once statsmodels publishes a 3.14 wheel (#202).

Candidate methods (good first issues):

• Generalized block (#104), local block (#105), and frequency-domain (#107) bootstraps.
• Wild and dependent-wild bootstraps, and a GARCH / volatility residual bootstrap.

Distributed execution (Dask / Spark / Ray), an async layer, and a string-keyed factory were considered and deliberately left out. The library is a CPU-bound, single-process toolkit.

See our good first issues for getting started.

1. Fork the tsbootstrap repository

2. Clone the fork to local:

git clone https://github.com/astrogilda/tsbootstrap

3. In the local repository root, sync the locked development environment with uv:

uv sync --extra dev

4. uv creates an isolated virtual environment from uv.lock and editable-installs the package, so changes to the package are reflected in your environment automatically. Run tools through the environment with uv run (for example uv run pytest).

5. Install the pre-commit hooks:

uv run pre-commit install

The hooks run ruff, formatting, and the other code-quality checks on each commit.

Verify the installation:

python -c "import tsbootstrap; print(tsbootstrap.__version__)"

This prints the installed version.

1. Create a new branch with a descriptive name (e.g., new-feature-branch or bugfix-issue-123).

git checkout -b new-feature-branch

2. Make changes to the project's codebase.
3. Commit your changes to your local branch with a clear commit message that explains the changes you've made.

git commit -m 'Implemented new feature.'

4. Push your changes to your forked repository on GitHub using the following command

git push origin new-feature-branch

5. Create a new pull request to the original project repository. In the pull request, describe the changes you've made and why they're necessary.

To run all tests, in your developer environment, run:

uv run pytest tests/

The sktime adapter classes can be validated with sktime's estimator checks:

from sktime.utils import check_estimator
from tsbootstrap.adapters import MovingBlockBootstrap

check_estimator(MovingBlockBootstrap)

This project is licensed under the ℹ️ MIT License. See the LICENSE file for additional info.

Contributors:

This project follows the all-contributors specification. Contributions of any kind welcome!

tsbootstrap implements bootstrapping methods for time series data. It generates resampled copies of univariate and multivariate series that preserve their chronological order and dependence structure.

Traditional bootstrap methods resample observations independently, which breaks the dependence in a time series: each observation usually depends on the ones before it. Time series bootstraps resample while preserving chronological order and correlation, so the resulting uncertainty estimates stay valid under that dependence.

tsbootstrap resamples either the observations directly (i.i.d. and block methods) or the innovations of a fitted model (residual and sieve methods), respecting the chronological order and dependence structure of the data.

Block methods resample blocks of consecutive observations to preserve short-range dependence. The block length defaults to the automatic Politis-White (2004) selection.

• Moving block (MovingBlock): overlapping fixed-length blocks (Kunsch 1989).
• Circular block (CircularBlock): blocks wrap around the series end (Politis-Romano 1992).
• Stationary block (StationaryBlock): geometric block lengths with independent uniform restart points (Politis-Romano 1994).
• Non-overlapping block (NonOverlappingBlock): disjoint blocks (Carlstein 1986).
• Tapered block (TaperedBlock(window=...)): blocks weighted by an energy-normalized window (Bartlett, Blackman, Hamming, Hann, or Tukey; Paparoditis-Politis 2001).

For dependent data with a good model fit, ResidualBootstrap(model=...) regenerates the series recursively from the fitted dynamics and resampled, centered innovations (not fitted + residuals). Supported models: AR, ARIMA, and VAR (multivariate). A non-stationary fit is refused (or skipped, per stability_policy) rather than producing explosive paths.

SieveAR selects an autoregressive order on the original series, then runs the AR recursion; suited to data with autoregressive structure.

Markov resampling, the distribution bootstrap, GARCH/volatility models, and frequency-domain / seasonal block methods are planned for a future version. The statistic-preserving method has been removed.