Resources about time series forecasting and deep learning.
C-Rank Grade: B (Solid) - 52/100
External PR Merge Rate: 100%
Response Time: 4d
First Timer Success: 100%
DaoSword/Time-Series-Forecasting-and-Deep-Learning has a recorded first-timer success rate of 100.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in DaoSword/Time-Series-Forecasting-and-Deep-Learning respond to incoming external pull requests in approximately 92.3 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 51.6 places DaoSword/Time-Series-Forecasting-and-Deep-Learning in the B tier.
The external contributor pull request merge rate for DaoSword/Time-Series-Forecasting-and-Deep-Learning is 100.0%, based on public PR activity from non-core contributors.
DaoSword/Time-Series-Forecasting-and-Deep-Learning does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Collaborative maintainer environment in DaoSword/Time-Series-Forecasting-and-Deep-Learning. Reviews community pull requests with focus on project quality.
Discussions in DaoSword/Time-Series-Forecasting-and-Deep-Learning focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
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/DaoSword/Time-Series-Forecasting-and-Deep-Learning?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 02:00 UTC (02:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 00:00 to 04:00 UTC (00:00 - 04:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~60 hours (96% approval). Large PRs (>500 lines) take ~443 hours (45% approval). Keep your diff compact for rapid review.
No cached good first issues currently tracked for DaoSword/Time-Series-Forecasting-and-Deep-Learning.
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 DaoSword/Time-Series-Forecasting-and-Deep-Learning, 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 DaoSword/Time-Series-Forecasting-and-Deep-Learning acknowledge new external contributions in approximately 3 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 100.0% were successfully merged into the primary branch.
DaoSword/Time-Series-Forecasting-and-Deep-Learning has a recorded first-timer success rate of 100.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~3 days+. 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 51.6 places DaoSword/Time-Series-Forecasting-and-Deep-Learning in the Solid tier.
The external pull request merge rate is 100.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
DaoSword/Time-Series-Forecasting-and-Deep-Learning does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for DaoSword/Time-Series-Forecasting-and-Deep-Learning 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.