{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"fev is a lightweight benchmarking library for time series forecasting models that provides standardized evaluation workflows, reproducible task definitions, and metric computation without heavy dependencies.","skillfed_tags":["time-series","benchmarking","forecasting"],"use_cases":["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."],"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.\n\nYou define a forecasting task once\u2014specifying dataset, horizon, covariates, and metrics\u2014then 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.","worth_installing":"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."},"id":"fev","links":{"html":"https://skillfed.io/packages/fev","md":"https://skillfed.io/packages/fev.md","pypi":"https://pypi.org/project/fev/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-01","license_spdx":null,"license_treatment":"permissive","name":"fev","python_support":"supports_current","summary":"fev: Forecast evaluation library"},"popularity":{"monthly_downloads":120073,"position":12044,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.0"}