fev
fev: Forecast evaluation library
Decision gist · record as of 2026-08-14
Yes. fev is actively maintained, has low install friction, carries permissive licensing, and fills a genuine gap in forecasting evaluation tooling. It is well-suited for researchers and practitioners who need reproducible benchmarking without the overhead of monolithic systems. The recent release cadence and active repository signal ongoing support.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.
- Datasets are loaded from Hugging Face Hub; internet access needed for default usage.
- Low install friction: pure Python wheel with only four runtime dependencies (datasets, numpy, pydantic, scipy).
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 permits commercial and derivative use with attribution. No restrictions on bundling or modification.
last release 2026-07-01 (44 days) · last repo commit 2026-08-14 · 167 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 120,073 downloads/mo, #12,044 on PyPI
Alternatives
Verify before relying
pip install fev
import fev
task = fev.Task(
dataset_path="autogluon/chronos_datasets",
dataset_config="m4_hourly",
horizon=24,
)
for window in task.iter_windows():
past_data, future_data = window.get_input_data()
task.evaluation_summary(predictions, model_name="my_model")- Whether adapters for popular forecasting libraries are production-ready or experimental.
- Performance characteristics when evaluating large-scale datasets or many models in sequence.
- Stability of the leaderboard infrastructure and submission process for contributed results.
What it is and what it does
fev is a benchmarking framework designed to standardize time series forecasting evaluation. It sits between standalone datasets (which offer no reproducibility guarantees) and monolithic end-to-end systems (which bundle models, data, and tasks tightly). The library provides Task objects that wrap datasets from Hugging Face Hub, define evaluation windows and horizons, and compute metrics consistently across different forecasting models.
You define a forecasting task once—specifying dataset, horizon, covariates, and metrics—then iterate over rolling evaluation windows to collect predictions from your model. fev handles the metric computation and produces an evaluation summary that uniquely identifies the task and captures results. Multiple summaries can be aggregated into leaderboards for model comparison. The library is built on top of datasets and scipy, keeping dependencies minimal while supporting point and probabilistic forecasting.
Use it for
- Benchmark your own forecasting model against standardized tasks to compare performance with other models.
- Create reproducible forecasting benchmarks that other researchers can run identically, ensuring comparable results.
- Aggregate results from multiple forecasting models into a leaderboard to rank them by skill score and win rate.
- Evaluate forecasting models on datasets stored on Hugging Face Hub without downloading data locally.
- Define custom forecasting tasks with specific horizons, covariates, and metrics for domain-specific evaluation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
fev is actively maintained, has low install friction, carries permissive licensing, and fills a genuine gap in forecasting evaluation tooling. It is well-suited for researchers and practitioners who need reproducible benchmarking without the overhead of monolithic systems. The recent release cadence and active repository signal ongoing support.
Install
fev on PyPI
Before you install
Low install friction: pure Python wheel with only four runtime dependencies (datasets, numpy, pydantic, scipy). Active maintenance with recent releases; last commit 2026-08-14.
Requires Python >=3.10. Datasets are loaded from Hugging Face Hub; internet access needed for default usage.
License in practice
Apache License 2.0 permits commercial and derivative use with attribution. No restrictions on bundling or modification.
Quickstart
pip install fev
import fev
task = fev.Task(
dataset_path="autogluon/chronos_datasets",
dataset_config="m4_hourly",
horizon=24,
)
for window in task.iter_windows():
past_data, future_data = window.get_input_data()
task.evaluation_summary(predictions, model_name="my_model")Verify before relying
- Whether adapters for popular forecasting libraries are production-ready or experimental.
- Performance characteristics when evaluating large-scale datasets or many models in sequence.
- Stability of the leaderboard infrastructure and submission process for contributed results.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesdatasetsnumpypydanticscipy |
| Maintenance | Actively maintained 44 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 120,073 / month, #12,044 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: fev-0.9.0-py3-none-any.whl
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See also chronos-forecasting · darts · utilsforecast · benchpots · asv · hierarchicalforecast · statsforecast · mlforecast · timesfm · datasetsforecast