tbats

library1.1.3pypypi✓ verified 22d ago

tbats is a Python library implementing BATS and TBATS models for time series forecasting, known for handling complex seasonality and Box-Cox transformations. The current version is 1.1.3, and it receives updates primarily for bug fixes, dependency upgrades, and occasional feature enhancements, with an irregular release cadence.

ai-ml data

pip install tbats

github ↗package ↗

INSTALL

IMPORT

SIG · TBATS

tbats

ai-mlpythonv1.1.3

Install

18.8s avg

Import

6400ms

Disk

442MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.1.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

build_error

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 18.8s · import 5.120s · 427MB

442MB installed

● package 442MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

TBATS

✓ from tbats import TBATS

This quickstart demonstrates how to initialize, fit, and forecast with a TBATS model. It generates a sample time series with trend and seasonality, then applies TBATS with Box-Cox transformation and ARMA errors to predict future values. `n_jobs=1` is used to prevent parallel processing in simple examples.

import numpy as np from tbats import TBATS # Generate some example time series data with seasonality np.random.seed(42) n_points = 100 seasonal_period = 24 # Daily seasonality t = np.arange(n_points) y = 50 + 2 * t + 10 * np.sin(2 * np.pi * t / seasonal_period) + np.random.normal(0, 5, n_points) # Create and fit the TBATS model estimator = TBATS(seasonal_periods=[seasonal_period], use_box_cox=True, use_trend=True, use_damped_trend=False, use_arma_errors=True, n_jobs=1) model = estimator.fit(y) # Make a forecast forecast = model.forecast(steps=10) print("Original series (last 5 points):", y[-5:]) print("Forecasted values (next 10 steps):", forecast)

Debug

Known issues

gotchaPrior to version 1.1.3, numeric precision issues could cause Box-Cox transformations to fail or produce incorrect results in certain scenarios.

fix

Upgrade to tbats version 1.1.3 or higher to resolve Box-Cox precision issues.

affects: <1.1.3

breakingIn version 1.1.0, the default method for spawning new processes for parallel execution (`n_jobs > 1`) was changed from the system default to 'spawn'. This may alter behavior, particularly on Windows or macOS where the default 'fork' method has different implications.

fix

If unexpected behavior related to multiprocessing occurs, explicitly set `multiprocessing_start_method` in the `TBATS` constructor (e.g., `TBATS(..., multiprocessing_start_method='fork')`) to match previous behavior or to 'fork' if that is preferred and supported by your OS.

affects: >=1.1.0

gotchaVersions prior to 1.0.6 (and specifically 1.0.5 had fixes) suffered from resource leaks where multiprocessing pools were not reliably closed, potentially leading to 'too many open files' errors or system instability.

fix

Upgrade to tbats version 1.0.6 or higher. If using older versions, ensure your application handles process pool management explicitly or restricts `n_jobs` to 1.

affects: <1.0.6

gotchaWhen `n_jobs=1` was specified in versions prior to 1.0.9, subprocesses might still have been triggered, leading to unnecessary overhead or potential issues with environments not configured for multiprocessing.

fix

Upgrade to tbats version 1.0.9 or higher to ensure that `n_jobs=1` correctly disables all subprocess spawning.

affects: <1.0.9

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'tbats'

The 'tbats' package is not installed in the Python environment.

fix

Install the package using 'pip install tbats'.

ImportError: cannot import name 'TBATS' from 'tbats'

The 'tbats' package is installed, but the import statement is incorrect.

fix

Use 'from tbats import TBATS' to import the TBATS class.

ValueError: seasonal_periods must be a list of positive numbers

The 'seasonal_periods' parameter is not provided as a list of positive numbers.

fix

Ensure 'seasonal_periods' is a list of positive numbers, e.g., 'seasonal_periods=[7, 365.25]'.

TypeError: fit() missing 1 required positional argument: 'y'

The 'fit' method is called without providing the time series data.

fix

Call 'fit' with the time series data, e.g., 'fitted_model = estimator.fit(y)'.

AttributeError: 'TBATS' object has no attribute 'forecast'

The 'forecast' method is called on an unfitted 'TBATS' object.

fix

Fit the model before forecasting, e.g., 'fitted_model = estimator.fit(y)' followed by 'y_forecasted = fitted_model.forecast(steps=14)'.

Upgrade

Version history

1.1.3latest on PyPI · released Apr 17, 2023

Audit

Dependencies

numpyrequiredNumerical operations and array handling.

scipyrequiredScientific computing, optimization, and statistical functions.

statsmodelsrequiredStatistical modeling framework, used for core time series components.

joblibrequiredFor parallel processing functionality (n_jobs > 1).

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/intive-DataScience/tbats ↗

packagehttps://pypi.org/project/tbats/ ↗