Aurora

⬇️ Download the desktop app

The easiest way to run Aurora — no Python, no terminal, no setup. Download the installer for your OS, double-click, and Aurora opens as a native app with the analysis backend bundled inside it.

→ Download the latest release

Pick the file that matches your OS (filenames carry the version, e.g. 0.2.0):

Aurora_aarch64.app.tar.gz is an auto-updater artifact, not a download — use the .dmg on macOS. There is currently no Intel-Mac (x86_64) or Linux AppImage build; Intel-Mac users can run from source.

On first launch Aurora bootstraps a small knowledge-bank seed, then runs fully offline — no API keys, no cloud, no telemetry. Drop a CSV on the window and watch it analyze. Aurora is local-first: your data never leaves your machine unless you explicitly share a single finding — see PRIVACY.md.

Heads up on the "Unknown Publisher" warning. Current releases are not yet code-signed, so Windows SmartScreen may show "Windows protected your PC" and macOS Gatekeeper may say "unidentified developer." This is expected for a young open-source project. On Windows: More info → Run anyway. On macOS: right-click the app → Open. Code-signing is on the roadmap.

Desktop app — tips & known quirks

A few things that are expected behavior, not bugs:

• First launch shows a welcome screen, not a run. That's intentional — a fresh start is clean. Drop a CSV (or click a demo card) to begin; your past runs are always available under Data → Bundles.
• Give the backend a few seconds on first launch. Aurora starts a local analysis server (127.0.0.1:8001) the first time you open it; the status dot reads "connecting…" for ~5–10 s before it goes live. If it lingers, the app is still warming up — it is not frozen.
• Big datasets take longer, and results land all at once. A large file can spend a while in "analyzing…". When it finishes you'll see "analysis complete" and the views populate. If the Overview looks momentarily empty right after completion, give it a few seconds or click another tab and back — the assembled state is still being fetched, and it will fill in.
• Re-running the same file is instant. Identical data + settings reuse a cached analysis; the banner reads "instant · cached." That's a speed win, not a skipped run.
• Knowledge Bank starts small on a fresh install. The bundled app ships with a seed bank that grows over time; the large multi-thousand-entry bank you may see in screenshots is built up on a machine that's been ingesting data for a while. Citations still work against whatever is local.

Want to build it yourself or run from source? Keep reading.

⚡ Quickstart (60 seconds)

# 1. Clone + create a virtualenvgit clone https://github.com/FantasyLab-ai/aurora.gitcd aurorapython -m venv .venv# Windows:.\.venv\Scripts\Activate.ps1# macOS / Linux:source .venv/bin/activate
# 2. Installpip install -r requirements.txt
# 3. Run the Studiopython studio_api.py

Open http://127.0.0.1:8000. Click ▶ Try a demo for an instant smoke test, or drop your own CSV / Parquet / JSON / XLSX.

First run will download a small knowledge-bank seed (~50 MB). After that, Aurora runs fully offline — no API keys, no cloud, no telemetry.

Optional extras (only if you need them):

pip install cryptography             # Ed25519 bundle signingpip install mcp                      # MCP server for LLM agents (Claude Desktop, Cursor, etc.)pip install -r requirements-dev.txt  # contributor / test extras

For the optional Vite + TypeScript frontend build (developers only), see § Optional frontend build.

🖥️ The desktop app

A native, installable app — frameless window, sidebar navigation, drag-and-drop any file onto it. Built with Tauri 2, with Aurora's full analysis backend bundled inside the installer, so it runs with zero setup: no Python, no venv, no terminal.

Most people should just download it. The rest of this section is for developers who want to build it from source or hack on the UI.

What it does

Build it from source / develop

The desktop app lives in desktop/. To build the installer yourself (PyInstaller backend + Tauri shell) or run the UI in hot-reload dev mode, see desktop/README.md — it covers the prerequisites (Node, Rust), the one-command launcher (launch.ps1), the installer build (build_installer.ps1), and the Windows Smart App Control caveat. A git tag vX.Y.Z push triggers the cross-platform release CI.

🚨 Aurora Sentinel — Decision Contracts in the room

Aurora ships a complete demo rig under demos/ that turns a Decision Contract trip into something tangible — a Discord ping, a Slack alert, an OBS overlay card, a smart-plug flipping in your room. Five "I gave my local AI X and watch what it caught" videos, all built on the same scaffolding.

Quickstart for the demos (after the install above):

# Generate the synthetic datasets one time:python -m demos.datasets.falling_ball.generatepython -m demos.datasets.server_metrics.generate
# Install the demo contracts into Aurora's contracts dir:cp demos/contracts/*.json ~/.aurora/decision_contracts/        # macOS / Linuxcopy demos\contracts\*.json $env:USERPROFILE\.aurora\decision_contracts\   # Windows
# Configure your webhooks once (copy the template + paste your URLs):cp demos/.env.demos.example demos/.env.demos                   # macOS / Linuxcopy demos\.env.demos.example demos\.env.demos                 # Windows
# Run the relay (it reads .env.demos for Discord/Slack URLs):python -m demos.relay.app

Full step-by-step recording walkthrough with OBS setup, contract installation, and a per-demo runbook lives at demos/README.md.

See Aurora in action

A real run on an environmental air-quality dataset — captured straight from a Studio session.

The moat: every sentence cites a source

_{Captured on a 9,357-row cross-sectional air-quality dataset at AUTO tier. Run took ≈14 seconds local on consumer hardware. 0 fabricated. 12 cited knowledge entries. Three methods deferred with reason.}

Verifying the install

After python studio_api.py starts, you should see something like:

[aurora] frontend = /path/to/aurora/frontend * Running on http://127.0.0.1:8000

Open the URL. The Studio greets you with a "drop a dataset" zone. Use any of:

data/fixtures/factory_bearing_demo.csv      # ships with the repo — bearing failuredata/fixtures/climate_buoy_demo.csv         # ships with the repo — NOAA-style buoy datadata/fixtures/patient_cohort_demo.csv       # ships with the repo — clinical cohort

Drop one, click AUTO + ▶ RUN ANALYSIS. In ~10-20 seconds you'll see:

• The six analytical lenses populate (Overview, Anomalies, Regimes, Motifs, Forecast, Physics)
• The Findings cards list every cited claim
• The 0 fabricated chip (always — that's Aurora's contract)

If that worked, you're production-ready.

Optional: enhanced frontend build (v0.10 Phase 2)

You do NOT need this to run Aurora. python studio_api.py is the only command required. This section is for developers who want type safety + a Vite hot-reload dev loop for future panel work.

The Phase 2 bundle adds a Vite + TypeScript layer with typed API helpers, hardened Server-Sent Events, and a Nanostores-backed state model. It runs side-by-side with the existing frontend and is fully non-breaking — the visible Studio looks identical with or without it.

Requirements: Node 18+ and npm.

Activation is a one-time build:

cd frontendnpm install              # installs Vite, TypeScript, @types/node, Nanostoresnpm run build            # produces frontend/dist/aurora.js + aurora.csscd ..

That's it. The next time anyone loads the Studio (whether Flask was already running or not), the bundle activates automatically — Flask's static route serves the new files, and index.html HEAD-probes for them on load. Open DevTools console after a page reload and you'll see ⚡ Aurora 0.10.0+phase2 loaded plus window.Aurora.api / .store / .stream available for custom panel work.

To turn it back off: rm -rf frontend/dist/ (or delete the folder). The page reverts to pure-legacy mode with no console warnings.

For active TypeScript development with hot-reload (two terminals):

# Terminal 1 — Flask backend on :8000python studio_api.py
# Terminal 2 — Vite dev server on :5173 with HMR + Flask proxycd frontend && npm run dev

Open http://127.0.0.1:5173 (not :8000). Edits to anything under frontend/src/ hot-reload instantly. Full guide: docs/frontend-build.md.

Status: v1.1 shipped (May 2026). v1.2 substantially shipped (streaming, cloud, Jupyter, contracts actions, runs library, MCP HTTP). v2.0 actively shipping on main (causal do-calculus, multi-dataset joins, Plugin SDK, custom KB ingestion, bundle attestation, KB marketplace, Kafka + Postgres CDC connectors, GPU embeddings). 599 tests passing locally; ~625 in CI. Real users running it on real data.

Run it in Docker (BYO-LLM)

cp .env.example .env           # pick a provider + paste your keydocker compose up              # → Aurora Studio at http://localhost:8000

One command, persistent state on the host (./aurora-data/), no cloud dependency. See docs/cloud-deploy.md for Fly / Railway / Render / VPS recipes + the new multi-tenant auth model.

One engine. Two surfaces. Same glass-box.

Aurora has two faces sharing one analytical engine. Same code, same principles, two integration shapes — one for humans clicking through findings, one for AI systems calling APIs.

🧠 Aurora Copilot — for humans

For analysts, quants, scientists, engineers.

A local quantitative copilot for the work that matters too much to trust to a model that hallucinates. Drop in a dataset and get rigorous findings — anomalies surfaced, causal relationships tested, forecasts with confidence bounds, every claim cited to the underlying computation. No cloud LLM guessing. No black-box math.

• Glass-box studio — six analytical lenses (Overview, Anomalies, Regimes, Motifs, Forecast, Physics), spacetime system graph, phase-space projection
• 24+ research-grade methods — Isolation Forest, robust z-score (Hampel), Granger, HMM Baum-Welch, persistent homology, SINDy, Gaussian processes, mutual information, VAR, DTW, BOCPD, Robust PCA, EMD, Kalman, Spectral entropy, and more
• Knowledge-grounded synthesis — every "What This Means" sentence cites a seed:* entry in a public, licensed knowledge bank
• Preflight data-quality — schema validation, missingness pattern detection, irregular-sampling check before any analysis runs (the data ok / N issues chip next to the fabricated counter)
• Causal inference (do-calculus) — Pearl-style backdoor identification + adjustment-OLS estimation. The legacy WHAT-IF panel now ships a "do() causal verdict" beneath every simulator output; the v2.0 LAB has a dedicated full-screen Causal tab for explicit treatment/outcome/intervention queries + counterfactuals
• Streaming / continuous mode — point Aurora at a directory; findings refresh as new data lands. Per-finding dedupe so the bus only fires on genuinely new findings; opt-in Decision Contracts auto-fire bridge. Kafka + Postgres CDC connectors for non-file sources (deps gated, listed in the v2.0 LAB → Connectors tab)
• Composable findings — a finished run's fitted physics / regime / baseline becomes a prior for the next run; aligned findings get a PRIOR badge
• Multi-dataset joins — pair two finished runs to see shared keys, schema compatibility, and inheritance candidates
• Runs Library — pin runs across sessions, A/B compare any two runs, share runs as portable .aurora.json bundles
• Custom KB ingestion — drop a folder of PDFs / TXT / MD; Aurora walks it, extracts text, folds chunks into your workspace KB. Browse-folder UI in the v2.0 LAB
• KB pack marketplace — install curated domain KB packs (Climate / Finance / Biomed / Industrial); preview-state packs labelled honestly
• Bundle attestation — three-check rollup (integrity + Ed25519 signature + trusted-signer registry) on any .aurora.json
• Honest disclosure — sampling, timeouts, and skipped methods are surfaced; never silently faked

→ See it in action: examples/factory-bearing/ → Conceptual overview: docs/concepts.md

🛡️ Aurora Cortex — for AI systems

For AI builders, agent developers, AI product teams.

The verification layer your AI agents and AI products call when they can't afford to hallucinate quantitative claims. Every LLM today invents numbers; Aurora is the structurally different fix — it computes and verifies rather than predicts. Connect via MCP, the Python SDK, or Decision Contracts.

Four programmable surfaces, all consuming the same .aurora.json bundle format:

import aurora_sdk as aurora
r = aurora.run("data.csv", depth="standard")r.findings.critical().by_method("iso-forest")r.forecast.peak(horizon_hours=24)r.bundle.save("audit.aurora.json")        # SHA-256 integrity + optional Ed25519 signing
# Verify on any machine with Aurora installedb = aurora.Bundle.load("audit.aurora.json")b.verify()                                  # raises if tampered

→ Wire Aurora into Claude Desktop in 5 minutes: examples/mcp-claude-desktop/ → Build a "fire when 3+ critical anomalies appear" automation: examples/decision-contracts/

Why Aurora exists

Every AI today — including the best cloud LLMs — hallucinates on quantitative claims. Bigger models don't fix it. RAG alone doesn't fix it. Chain-of-thought just produces longer confident lies.

Aurora is the rarest kind of fix — structurally different. It computes and verifies rather than predicts. It runs locally on your machine. It shows its math. Every relationship Aurora reports is computed, not guessed. Every claim cites its source. Every uncertain finding is rendered as uncertain — never confident-looking math over shaky ground.

Core principles

• 🔍 Glass-box at every layer. Every node, edge, finding, and recommendation traces to its source. Bundles carry a SHA-256 content hash and can be Ed25519-signed.
• 💻 Local-first, always. Your data stays on your machine. No telemetry. No phone-home. The MCP server enforces a per-call path allowlist; Decision Contracts block private-IP webhook targets by default.
• 🎯 Honesty rule. Uncertain relationships render as uncertain. When methods sample or time out, the user is told. Aurora's fabricated_count chip is a contractual 0 — and it's audited live.
• 📖 Open source. Apache 2.0. Inspectable. Forkable. Yours to audit, vendor, embed, redistribute.

Calibrated, not just cited

A referee that can't quantify its own error rate isn't a referee. As of v0.10.0, Aurora ships a calibration engine: for the changepoint family (BOCPD, CUSUM, PELT), every finding carries the empirically measured rate at which that detector fires on data containing nothing to find — data shaped like yours.

The measured headline, from Aurora's own production CUSUM detector (n=90, 4,000 seeded null trials per cell): it fires on 0.1% of independent stationary series, 48.3% at lag-1 autocorrelation φ=0.6, and 89.5% at φ=0.8. Autocorrelation that ordinary for daily demand or sensor data turns a "detection" into a coin flip — and without calibration, nothing downstream can tell. It compounds with length, too: at φ=0.4 the rate climbs from 13.6% (n=90) to 39.6% (n=180) — collecting more data makes this failure mode worse, not better. Meanwhile the same corpus shows what does not fool the detectors: a strong 7-day seasonal cycle (0 fires in 4,000 trials) and heavy-tailed noise (0.4%) barely register. Autocorrelation is the killer, and now it's measured.

So Aurora tells you, at the moment the finding is produced:

• the measured false-fire rate with a Wilson 95% interval, matched to your data's fingerprint (length, bias-corrected autocorrelation, tails, seasonality, missingness)
• an honest status — calibrated, interpolated (with disclosed max error), unavailable (outside the measured envelope, offending dimension named), or not_yet_calibrated — never a silent default
• a verdict downgrade: findings the data cannot support flip to not_identifiable, with the raw statistic still reported and the configured ceiling disclosed
• derived remediation — required consecutive confirmations, pre-whitening with the estimated φ — only where the math supports it
• the corpus version + SHA-256 in the finding and the bundle, so a skeptic can verify which table produced the claim

Watch it catch a constraint-legal action taken for a phantom reason:

python -m demos.phantom_signal.run_demo

Details, schema, and the full honesty ruleset: docs/CALIBRATION.md. What it does not claim: no power/sensitivity numbers, no real-data ground truth, and corpus v1 covers the changepoint family — the other methods say not_yet_calibrated out loud instead of pretending.

How it connects

Aurora is built to be infrastructure both humans and AI systems can call:

• MCP Server — connect any MCP-compatible AI client (Claude Desktop, Claude Code, Cursor, custom agents). 7 tools advertised; path-allowlisted; 2 MB output cap; JSON-only error wrapping
• Python SDK — import aurora_sdk in notebooks, scripts, pipelines, agent frameworks
• Decision Contracts — programmable predicates fire actions (webhook / log / append-only file). SSRF-guarded, rate-limited, audited
• Webhooks — deliver verified findings to Slack / Discord / PagerDuty / your own intake API (Slack/Discord/PagerDuty action types coming in v1.2; generic webhook ships now)
• Local Studio — full glass-box exploration of findings, intelligence panels, system graph, cube navigator
• Aurora Bundle Format v1 — a portable, signable, citeable .aurora.json artifact that every layer above produces and consumes

Quick examples

As a Copilot — drop a CSV, read what Aurora found

python studio_api.py# Open http://127.0.0.1:8000 → click "Try a demo → factory_bearing_demo"# 10 seconds later: 11 cited findings, 3 critical, confidence 84%, 0 fabricated.

As a Cortex — give Claude Desktop the ability to cite real math

Add to your claude_desktop_config.json:

{  "mcpServers": {    "aurora": {      "command": "uvx",      "args": ["aurora-mcp", "--allow-root", "/Users/you/data"]    }  }}

No install step — uvx fetches aurora-mcp from PyPI on first run. (Running from a source checkout instead? Use "command": "/path/to/aurora/.venv/bin/python", "args": ["-m", "aurora_mcp.server", "--allow-root", "/Users/you/data"].)

Then ask Claude:

Run Aurora on /Users/you/data/factory_bearing.csv at standard depth, then drill into the most critical anomaly with full evidence.

Claude chains aurora_analyze → aurora_findings(severity=crit) → aurora_explain(claim_id=…) and writes you back a response citing every method — never inventing a row number or a z-score.

Full walkthrough: examples/mcp-claude-desktop/

As a Cortex — wire findings into automation

~/.aurora/decision_contracts/bearing-watch.json:

{  "id": "bearing-watch",  "trigger": {"field": "findings.crit_count", "op": ">=", "value": 3},  "actions": [    {"type": "webhook", "url": "https://hooks.example.com/aurora"},    {"type": "file", "path": "alerts.jsonl"}  ],  "rate_limit": {"max_per_hour": 12}}

from aurora_sdk import Bundlefrom fantasyai.aurora.decision_contracts import load_contracts, fire_contract
bundle = Bundle.load("latest.aurora.json").docfor c in load_contracts():    fire_contract(c, bundle)               # webhook fires, audit row appended

What's working today

The v1.1 substrate — Bundle Format, SDK, MCP, Decision Contracts, Research Kit, six analytical lenses — is shipped and stable. The v1.2 sprint is substantially complete; v2.0 work is landing continuously on main.

Analytical methods (17 → 24+):

• v1.1: Isolation Forest, Hampel z-score, HMM, mutual information, Granger, wavelet, Lomb-Scargle, Gaussian process, persistent topology, SINDy, multivariate outliers, cluster stability, Bayesian, panel, survival, spatial, network, DiD
• v1.2 added: VAR, DTW, BOCPD, Robust PCA, EMD, Kalman, Spectral entropy — each with skip reasons + glass-box compliance

v1.2 surfaces (mostly shipped):

• Streaming / continuous mode — /api/stream/start|stop|status|events with SSE event bus, per-finding dedupe, opt-in Decision Contracts bridge, Live Findings strip in the Studio
• Aurora Cloud (Phase 1 + 2) — Docker + docker-compose + cloud-deploy guide. Multi-tenant auth + per-workspace data isolation + usage logging behind AURORA_AUTH_REQUIRED=1
• BYO-LLM — pluggable provider abstraction with 5 backends (Anthropic / OpenAI / Gemini / Ollama / OpenAI-compatible)
• Decision Contracts: Slack / Discord / Email actions — extend the v1.1 webhook / log / file types. SSRF + recipient caps + URL token redaction
• Runs Library UI — pin / A/B compare (via the joins endpoint) / share-as-bundle, all from a top-toolbar chip
• MCP HTTP transport — /mcp/v1/* endpoints for remote agents that can't subprocess locally; standalone server mode with optional token gate
• Preflight data-quality — schema + missingness + irregular-sampling checks. data ok / N issues pill alongside the fabricated chip
• Jupyter integration — aurora.run(df) with rich HTML reprs, to_html_report(), sample notebook
• KB pack distribution — manifest + downloader + 4 starter packs + registry auto-detection

v2.0 surfaces (shipping, exposed in the ⚡ v2.0 LAB modal):

• Causal inference (do-calculus) — fantasyai/aurora/causal/. Backdoor identification + adjustment-OLS estimation + counterfactual queries. The legacy WHAT-IF panel now also returns a "do() causal verdict" beneath every simulator output
• Multi-dataset joins — /api/joins/analyze produces shared keys + schema compatibility + cross-correlation hints + inheritance candidates between two finished runs
• Composable findings — extract priors from a finished run, inherit them into the next; aligned findings get a PRIOR · matches / drifts / novel badge
• Plugin SDK — third-party methods register via aurora_plugins entry-point group; same contract as built-ins; failures are isolated
• Custom KB ingestion — drop a folder of PDFs / TXT / MD; Aurora walks it and folds chunks into the workspace KB. Folder picker + drag-and-drop in the v2.0 LAB
• Bundle attestation — three-check rollup (integrity + Ed25519 signature + trusted-signer registry) on any .aurora.json
• KB pack marketplace — install curated domain KB packs (Climate / Finance / Biomed / Industrial). Preview-state packs honestly labelled "not yet released"
• Streaming connectors — Kafka + Postgres CDC connectors for non-file sources. Deps are gated; the v2.0 LAB → Connectors tab lists install hints
• GPU embeddings — AURORA_EMBEDDINGS_DEVICE=cuda|mps|auto env-var device selection with graceful CPU fallback

Counts:

• 599 tests passing locally; ~625 in CI (scipy + flask-dependent tests run there)
• 16 backend modules import cleanly
• 0 fabricated findings — contractual

Local execution end-to-end. No cloud dependency after initial knowledge bank download.

What's rough

Named honestly:

• The knowledge bank ships with a seed set; the full pack is downloaded separately and is still expanding
• The 4 v2.0 marketplace packs (Climate / Finance / Biomed / Industrial) are reserved in the manifest but not yet hosted — SHAs are PENDING_FIRST_BUILD. The Studio labels them PREVIEW and disables install until the next manifest revision
• Some advanced methods skip on datasets that lack required structure (no time axis, no entity column) — these skips are correct glass-box behavior; can be confusing without reading the skip reason
• Per-method timeouts (90 s default) defer some methods on very large datasets; disclosed honestly
• Mobile / tablet responsiveness still pending
• Browser security prevents Aurora from reading absolute folder paths from a <input type="file"> picker; KB ingest seeds the folder name + asks the user to type the absolute path

Build-in-public log: CHANGELOG.md.

Built in the open. By a real person. For real work.

Aurora is fully open source under Apache 2.0. The engine, the schema, the baseline templates, the MCP server, the SDK, the webhook layer — all of it. No black box at any layer, including the codebase itself.

The roadmap is public. The build is documented on YouTube. Domain experts who contribute knowledge bank entries or templates will be able to earn from the upcoming marketplace (v2.0). Aurora gets smarter as the community grows.

• ⭐ Star on GitHub — visibility
• 💜 Back on Patreon — recurring support funds the build
• 📺 Follow the build on YouTube
• 🎬 Daily clips on TikTok
• 🐦 Daily progress on X

Roadmap

See ROADMAP.md for the full picture. Recent progress:

• v1.2 — substantially shipped: Streaming Phase 1+2 · Aurora Cloud Phase 1+2 (Docker + BYO-LLM + multi-tenant auth) · 7 new analytical methods · Jupyter integration · KB pack distribution · Preflight data-quality · Slack/Discord/Email contract actions · Runs Library UI · MCP HTTP transport
• v2.0 — actively shipping on main: Causal inference (do-calculus) · Multi-dataset joins · Composable findings · Plugin SDK · Custom KB ingestion (PDFs → KB) · Bundle attestation service · KB pack marketplace (preview) · Kafka + Postgres CDC streaming connectors · GPU embedding device gate
• v1.2 tail (still open): Bundle Format v1.1 (signed delta updates) · conversational copilot polish · KB community contribution flow · domain template packs · DuckDB ingest · mobile/tablet responsiveness pass
• v2.0+ (6–12 months): Federated knowledge contribution · marketplace with creator revenue share · Aurora kernels · Aurora-as-CI · Web Component embeds · mobile companion

Project Family

Aurora is part of FantasyLab.ai — local-first AI tools for serious work. Sister project:

• Fantasy Studio — AI-directed cinematic 3D rendering using real path-traced light simulation. Same philosophy applied to creative work instead of analytical work.

Contributing

We welcome contributions. See CONTRIBUTING.md for setup, code style, testing requirements, and the development workflow.

Good places to start:

• Issues labeled good first issue
• Issues labeled help wanted
• Knowledge bank contributions — adding cited entries for new domains. See docs/knowledge-bank.md
• Aurora MCP integrations — example notebooks, demo agents

Security

Aurora processes data locally. The SDK and MCP server have no telemetry, no phone-home, no analytics. The verification cortex is the moat — and it's audited. See SECURITY.md for the full security model and how to report a vulnerability.

License

Aurora is licensed under Apache License 2.0. Use it commercially, modify it, redistribute it. Just keep the copyright notice and don't claim we endorse your derivative.

For deployment in customer-managed cloud environments with enterprise support, contact [email protected].

Acknowledgments

Aurora stands on the shoulders of decades of statistical and analytical research. Citations are baked into every Aurora output. Foundational methods come from researchers including:

• Vipin Chandola, Arindam Banerjee, Vipin Kumar (anomaly detection)
• Judea Pearl (causal inference)
• Rob Hyndman, George Athanasopoulos (forecasting)
• Steven Brunton, Joshua Proctor, Nathan Kutz (SINDy / sparse identification)
• Frank Hampel (robust statistics, 1974)
• Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou (Isolation Forest)
• Carl Edward Rasmussen, Christopher K. I. Williams (Gaussian processes)
• C. W. J. Granger (causality)
• L. E. Baum, T. Petrie, G. Soules, N. Weiss (Baum–Welch HMM)
• Thomas Malthus (exponential dynamics, 1798)
• Reverend Thomas Bayes (Bayesian inference)
• …and the broader open scientific community whose work makes Aurora possible

The Aurora project itself is built as part of FantasyLab.ai.