{"id":49231369,"url":"https://github.com/heraclitus0/qsi","last_synced_at":"2026-04-24T12:07:46.305Z","repository":{"id":304494658,"uuid":"1016490305","full_name":"heraclitus0/QSI","owner":"heraclitus0","description":"A real-time rupture detection tool for identifying forecast vs. actual drift and preventable 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returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adaptive-thresholding","control-systems","drift-detection","real-time-analysis","rupture-detection","streamlit"],"created_at":"2026-04-24T12:07:43.831Z","updated_at":"2026-04-24T12:07:46.300Z","avatar_url":"https://github.com/heraclitus0.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"QSI_logo.png\" alt=\"QSI Logo\" width=\"120\"/\u003e\n\u003c/p\u003e\n\n\u003ch1 align=\"center\"\u003eQuantitative Stochastic Intelligence\u003c/h1\u003e\n\n\u003cp align=\"center\"\u003e\n  Adaptive rupture detection and epistemic diagnostics for dynamic systems.\u003cbr/\u003e\n  \u003cstrong\u003ePolicy-calibrated intelligence that learns from volatility.\u003c/strong\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n\u003ca href=\"https://zkvyksd6zuzfyaqshzphzm.streamlit.app/\" target=\"_blank\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Streamlit-Live-limegreen\" alt=\"Streamlit App\"/\u003e\n\u003c/a\u003e\n  \u003cimg src=\"https://img.shields.io/badge/License-MIT-blue.svg\" alt=\"MIT License\"/\u003e\n  \u003ca href=\"https://hits.sh/github.com/heraclitus0/qsi/\"\u003e\n    \u003cimg src=\"https://hits.sh/github.com/heraclitus0/qsi.svg?style=flat-square\" alt=\"Visitor Count\"/\u003e\n  \u003c/a\u003e\n\u003c/p\u003e\n\n\n---\n\n## Overview\n\nQSI is a **decision-intelligence engine** that detects ruptures in forecast vs. actual performance, quantifies preventable losses, and provides **epistemic diagnostics** such as drift, threshold breaches, stability scope, and policy sensitivity.  \n\nIt is designed for **board-level clarity** and **field-level adaptability**, aligning with volatile domains ranging from **supply chains** to **finance, cyber, and pharma**.  \n\nMinimal, calibrated, and transparent — QSI surfaces actionable intelligence without black-box opacity.\n\n---\n\n## Features\n\n- **Rupture Detection** — Tracks forecast vs. actual drift, thresholds, and breach events.  \n- **Loss Quantification** — Converts drifts into monetary loss using unit cost.  \n- **Epistemic Diagnostics** — Scope score, PSI, and breach ETA forecasting.  \n- **Cognize Meta-Policy** — Optional adaptive mode with exploration and policy promotion.  \n- **Segment Graphs** — Coupled dynamics across multiple SKUs or regions.  \n- **Dynamic Configurability** — Every knob is exposed for user calibration, no statics hard-coded.  \n- **Custom Models** — Plug in enterprise-specific threshold policies via registry.  \n\n---\n\n## Interface\n\n![QSI Interface](graphs/rupre_plot.png)\n\n---\n\n## Use Cases\n\nQSI is **domain-agnostic**. Example applications include:  \n\n- **Supply Chains** — Prevent procurement losses by catching over/under-forecast drifts early.  \n- **Finance** — Stress-test trading strategies against volatility thresholds.  \n- **Healthcare \u0026 Pharma** — Detect demand misalignments in critical drug or equipment supply.  \n- **Cybersecurity** — Monitor deviations in expected traffic or anomaly baselines.  \n- **Operations \u0026 Strategy** — Track policy adherence, systemic drift, and rupture clusters.  \n\n---\n\n## Quick Start\n\n1. **Try it live**: [Launch QSI on Streamlit](https://zkvyksd6zuzfyaqshzphzm.streamlit.app/)  \n2. **Upload your data**: CSV with columns → `Date, Forecast, Actual, Unit_Cost`.  \n3. **Explore outputs**:  \n   - Ruptures flagged with drift × cost loss quantification  \n   - Policy-calibrated thresholds \u0026 diagnostics (Scope, PSI, ETA)  \n   - Interactive drift vs threshold plots \u0026 volatility bands  \n4. **Dive deeper**:  \n   - [Case Study — Hyderabad Pilot](QSI_case_study.md)  \n   - [Full Analytical Report](QSI_project_report.md)  \n   - [User Guide](USER_GUIDE.md)\n\n\n---\n\n\n## Disclaimer\n\nQSI outputs are **calibration-dependent**.  \nThe toggles and parameters exist for a reason: to adapt the system to the volatility profile of your domain.  \nMisuse without domain calibration may lead to misleading results.  \n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fheraclitus0%2Fqsi","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fheraclitus0%2Fqsi","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fheraclitus0%2Fqsi/lists"}