mlforecast
Scalable machine learning based time series forecasting
Decision gist · record as of 2026-08-14
Yes. mlforecast is actively maintained, has no known vulnerabilities, and provides a production-ready framework for time series forecasting at scale. It combines efficient feature engineering with optional distributed training. Install if you need to forecast multiple time series with machine learning models; skip if you need deep learning or univariate statistical methods only.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; input data must be a pandas DataFrame in long format with columns for unique_id, ds (timestamp), and y (target value).
- Low install friction; pure Python wheel with 8 runtime dependencies including pandas, coreforecast, and optuna.
- Active maintenance with a release 35 days ago and ongoing commits.
License · maintenance · safety
Apache Software License 2.0 (permissive) — Apache Software License 2.0 is permissive; you can use, modify, and distribute mlforecast freely in commercial and private projects provided you include the license notice.
last release 2026-07-10 (35 days) · last repo commit 2026-08-12 · 1,265 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 494,140 downloads/mo, #6,352 on PyPI
Alternatives
Verify before relying
pip install mlforecast
from mlforecast import MLForecast
import pandas as pd
fcst = MLForecast(
models=[model1, model2],
freq='D',
lags=[7, 14],
date_features=['dayofweek']
)
fcst.fit(series)
predictions = fcst.predict(14)- Whether distributed training via Dask, Ray, or Spark requires additional cluster setup beyond the base install.
- Performance characteristics when forecasting millions of time series—the description claims efficiency but provides no benchmarks.
- Whether Conformal Prediction for prediction intervals requires additional dependencies or configuration.
What it is and what it does
mlforecast is a time series forecasting framework that trains regressors on multiple series simultaneously. It automates feature engineering—lag creation, rolling statistics, date features—and handles the recursive prediction strategy needed to forecast multiple steps ahead. The core workflow is: load data in long format (one row per series-timestamp pair), define models and features, call fit() to train, then predict(n) to generate n-step forecasts.
The package is designed for production use and scales to large datasets through optional integration with Dask, Ray, or Spark clusters. It supports exogenous variables, static covariates, probabilistic forecasting via Conformal Prediction, and cross-validation for model evaluation. Dependencies include pandas for data handling, coreforecast for optimized feature computation, narwhals for dataframe abstraction, and optuna for hyperparameter optimization.
Use it for
- Train models across multiple time series and generate multi-step forecasts with automatic lag feature updates.
- Optimize hyperparameters across multiple models using cross-validation splits cached during training.
- Forecast demand by combining lag features, rolling statistics, and exogenous variables.
- Distribute training across a Dask or Ray cluster to handle large numbers of time series.
- Generate prediction intervals using Conformal Prediction to quantify forecast uncertainty.
- Pretrain a model on one set of series and fine-tune on a different series with limited history.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
mlforecast is actively maintained, has no known vulnerabilities, and provides a production-ready framework for time series forecasting at scale. It combines efficient feature engineering with optional distributed training. Install if you need to forecast multiple time series with machine learning models; skip if you need deep learning or univariate statistical methods only.
Install
mlforecast on PyPI
Before you install
Low install friction; pure Python wheel with 8 runtime dependencies including pandas, coreforecast, and optuna. Active maintenance with a release 35 days ago and ongoing commits.
Requires Python 3.10 or later; input data must be a pandas DataFrame in long format with columns for unique_id, ds (timestamp), and y (target value).
License in practice
Apache Software License 2.0 is permissive; you can use, modify, and distribute mlforecast freely in commercial and private projects provided you include the license notice.
Quickstart
pip install mlforecast
from mlforecast import MLForecast
import pandas as pd
fcst = MLForecast(
models=[model1, model2],
freq='D',
lags=[7, 14],
date_features=['dayofweek']
)
fcst.fit(series)
predictions = fcst.predict(14)Verify before relying
- Whether distributed training via Dask, Ray, or Spark requires additional cluster setup beyond the base install.
- Performance characteristics when forecasting millions of time series—the description claims efficiency but provides no benchmarks.
- Whether Conformal Prediction for prediction intervals requires additional dependencies or configuration.
Package facts
| License | Apache Software License 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagescloudpicklecoreforecastfsspecnarwhalsoptunapandasscikit-learnutilsforecast |
| Maintenance | Actively maintained 35 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 494,140 / month, #6,352 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: mlforecast-1.1.0-py3-none-any.whl
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