What is migas-1.5?
42/100
Trust Score (E)
⚠️ Use Caution
migas-1.5 is a time-series-forecasting that An AI model. It has a Nerq Trust Score of 42/100 (E). 8 GitHub stars. Published by Unknown. Last analyzed September 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 8 stars, 3.1K downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
42
TRUST SCORE
E
GRADE
8
STARS
3.1K
DOWNLOADS
What migas-1.5 Does
migas-1.5 is a model in the time-series-forecasting category. An AI model. It is published by an independent developer and has no specified license. With 8 GitHub stars and 3.1K downloads, it has a small community of users and contributors.
Who Should Use migas-1.5
migas-1.5 is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | Unknown |
|---|---|
| Category | time-series-forecasting |
| License | Not specified |
| Type | model |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=synthefy-migas-1-5
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| timesfm-3.0-pytorch | 64 | 232 |
| Toto-2.0-2.5B | 44 | 49 |
| t0-alpha | 44 | 34 |
| Timer-S1 | 44 | 33 |
| TiRex-2 | 42 | 33 |
Frequently Asked Questions
What is migas-1.5 used for?
migas-1.5 is a time-series-forecasting tool. An AI model.
Is migas-1.5 free?
License: Check project page. migas-1.5 has 8 GitHub stars.
Is migas-1.5 safe?
migas-1.5 has a Nerq Trust Score of 42/100 (E). Evaluate carefully.
What are alternatives to migas-1.5?
Top alternatives: timesfm-3.0-pytorch, Toto-2.0-2.5B, t0-alpha. See full comparison.
Safety Report Is It Safe?
Alternatives Prediction
Trending Leaderboard
Discover MCP Servers
Packages Models
Stats API
Last updated September 2026. Trust scores based on automated analysis of public data.