Time series forecasting with PyTorch
C-Rank Grade: B (Solid) - 46/100
External PR Merge Rate: 26%
Response Time: 23h
First Timer Success: 18%
sktime/pytorch-forecasting has a recorded first-timer success rate of 17.9%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in sktime/pytorch-forecasting respond to incoming external pull requests in approximately 23.3 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 46.2 places sktime/pytorch-forecasting in the B tier.
The external contributor pull request merge rate for sktime/pytorch-forecasting is 26.0%, based on public PR activity from non-core contributors.
sktime/pytorch-forecasting currently has 13 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/sktime/pytorch-forecasting?utm_source=github&utm_medium=badge)
Enter your branch size and test coverage below to forecast review turnaround and get 1-click git splitting commands.
This repository currently has 13 open good first issue tickets available for newcomers.
Rate what actually matters after opening a pull request here.
Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.
When evaluating whether to contribute to sktime/pytorch-forecasting, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of sktime/pytorch-forecasting acknowledge new external contributions in approximately 23 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 26.0% were successfully merged into the primary branch.
sktime/pytorch-forecasting has a recorded first-timer success rate of 17.9%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~23 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 46.2 places sktime/pytorch-forecasting in the Solid tier.
The external pull request merge rate is 26.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, sktime/pytorch-forecasting currently has 13 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for sktime/pytorch-forecasting are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.