Scalable and user friendly neural :brain: forecasting algorithms.
⚡ Executive Summary & Quick Facts
Authoritative Sourceneuralforecast is neuralforecast is an open-source project written in Python: Scalable and user friendly neural :brain: forecasting algorithms..
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of November 2026), Nixtla/neuralforecast has accumulated 4,286 GitHub stars and 512 forks. It is written in Python, licensed under Apache-2.0, and ranks #3,023 globally. It is actively maintained with the latest commit recorded on Oct 14, 2026.”
| Repository | Nixtla/neuralforecast ↗ |
|---|---|
| Primary Category | baselines |
| Language & Stack | Python |
| Software License | Apache-2.0 |
| Community Traction | ★ 4.3K stars · ⑂ 512 forks · #3,023 globally |
| Maintenance | ● Active (Last commit: Oct 14, 2026) · 12 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
🛡️ Repository Maintenance & Health Audit (2026)
Health Score: 92/100 · Grade AYes, neuralforecast is actively maintained. Continuous telemetry recorded the last code commit on Oct 14, 2026 (20 days ago). Supported by 4,286 stars and 512 community forks, this repository holds a calculated health index of 92/100, reflecting regular maintenance (20 days ago) and healthy issue cadence.
Star History
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+107
STARS · LAST 30 DAYS
4
PER DAY
#462
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +0 | +107 | +360 |
| Per day | 1 | 4 | 4 |
| Forks gained | +0 | +10 | +26 |
neuralforecast gained 107 stars in the last 30 days, about 4 a day, and now has 4.3K. It is about 6 years old and has averaged roughly 714 stars a year. It ranks #462 among Python repositories and #3,023 across all languages on GitHubRepo.
Trending Record
19
DAYS ON TRENDING
#5114
BEST RANK
Oct 14, 2026
FIRST APPEARANCE
Active
STATUS TODAY
neuralforecast has maintained a continuous presence across global trending indexes, peaking at #5114. Below is the 30-day activity profile:
💡 Overview
neuralforecast is an open-source project written in Python: Scalable and user friendly neural :brain: forecasting algorithms.
Engineered for speed, consistency, and developer ease, it solves common hurdles in baselines, baselines-zoo, deep-learning. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install neuralforecast⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
neuralforecast coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed neuralforecast into Python backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open Apache-2.0 license.
🚀 Getting Started
Install neuralforecast using your package manager: `pip install neuralforecast`
Initialize your project workspace or configuration file for neuralforecast.
Import neuralforecast into your codebase or invoke it directly from your terminal.
Execute your test suite or run `neuralforecast --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
Nixtla/neuralforecast is currently ranked #3,023 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
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Frequently Asked Questions
What does neuralforecast do? +
Scalable and user friendly neural :brain: forecasting algorithms.
Is neuralforecast actively maintained in 2026? +
Yes, neuralforecast is actively maintained. The latest code push was recorded on Oct 14, 2026 (20 days ago). It holds a calculated Maintenance Health Score of 92/100 (A rating) with 4,286 stars and 12 open issues.
What language is neuralforecast written in? +
The primary language is Python. Topics include: baselines, baselines-zoo, deep-learning, deep-neural-networks, deepar.
How many stars does neuralforecast have? +
neuralforecast has 4,286 stars and 512 forks on GitHub.
How does neuralforecast rank among GitHub repositories? +
With 4,286 stars, Nixtla/neuralforecast is ranked #3,023 globally across all repositories tracked on GitHubRepo and #462 among Python projects.
What license is neuralforecast distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
