skforecast

library0.22.0pypypiunverified

Skforecast is a Python library for time series forecasting using statistical and machine learning models. It works with any estimator compatible with the scikit-learn API, including LightGBM, XGBoost, CatBoost, Keras, and many others. Current version is 0.22.0, with a release cadence of roughly quarterly.

ai-ml data

pip install skforecast

github ↗package ↗homepage ↗docs ↗changelog ↗

INSTALL

IMPORT

SIG · SKFORECAST

skforecast

ai-mlpythonv0.22.0

Install

22.4s avg

Import

—

Disk

599MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.22.0 · 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 22.4s · import 0.000s · 580MB

599MB installed

● package 599MB

Code

Verified usage

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

ForecasterAutoreg

✓ from skforecast import ForecasterAutoreg

✗ from skforecast.recursive import ForecasterAutoreg

Basic usage: create a ForecasterAutoreg with a random forest regressor and 10 lags, fit on a univariate series, and predict 5 steps ahead.

import numpy as np import pandas as pd from skforecast.recursive import ForecasterAutoreg from sklearn.ensemble import RandomForestRegressor # Example data y = pd.Series(np.random.rand(100), index=pd.date_range(start='2020-01-01', periods=100, freq='D')) # Create forecaster forecaster = ForecasterAutoreg( regressor=RandomForestRegressor(random_state=123), lags=10 ) # Fit forecaster.fit(y=y) # Predict predictions = forecaster.predict(steps=5) print(predictions.head())

Debug

Known issues

breakingIn v0.22.0, model_selection module was reorganized. Functions like `grid_search_forecaster` and `random_search_forecaster` are removed. Use `BacktestGridSearch` and `BacktestRandomSearch` instead.

fix

Replace `from skforecast.model_selection import grid_search_forecaster` with `from skforecast.model_selection import BacktestGridSearch` and adjust usage.

affects: >=0.22.0

breakingIn v0.21.0, `TimeSeriesDifferentiator` moved from `skforecast.utils` to `skforecast.preprocessing`. Old import will raise ImportError.

fix

Change import to `from skforecast.preprocessing import TimeSeriesDifferentiator`.

affects: >=0.21.0

deprecated`ForecasterAutoregDirect` is deprecated in favor of `ForecasterAutoregMultiOutput` since v0.12.0.

fix

Use `ForecasterAutoregMultiOutput` with steps parameter.

affects: >=0.12.0

gotchaWhen using `ForecasterAutoregCustom`, the custom `create_lags` function must return a numpy array of shape (len(y), len(lags)). Wrong shape leads to silent errors.

fix

Ensure function returns array with correct dimensions; test with a small dataset first.

affects: all

gotcha`ForecasterAutoreg.fit()` expects `y` as a pandas Series. Passing a numpy array or DataFrame with multiple columns will raise ValueError.

fix

Always convert to pd.Series: `y = pd.Series(y_array)`.

affects: all

deprecated`ForecasterAutoreg` parameter `selector_step` is deprecated since v0.18.0. Use `feature_selection` argument in `fit()` instead.

fix

Remove `selector_step` and pass `feature_selection=True` to `fit()`.

affects: >=0.18.0

Upgrade

Version history

0.22.0latest on PyPI · released Apr 23, 2026

Audit

Dependencies

scikit-learnrequiredCore dependency for base estimators and utilities.

numpyrequiredArray and numerical operations.

pandasrequiredData manipulation and time series handling.

joblibrequiredCaching and parallel processing.

numbarequiredJust-in-time compilation for performance.

optunaoptionalOptional hyperparameter tuning.

xgboostoptionalOptional booster.

lightgbmoptionalOptional booster.

catboostoptionalOptional booster.

Agent activity

15 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/skforecast/skforecast ↗

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

homepagehttps://www.skforecast.org ↗

docshttps://www.skforecast.org ↗

changeloghttps://skforecast.org/latest/releases/releases ↗