Omen — An Agentic Time Series Forecasting Platform

The Omen e-book is out! Check out Agentic Time Series Forecasting for Supervillains, where I teach sound forecasting practice in an elegant, step-by-step manner.

Omen gives an AI agent a tested, statistically rigorous toolkit for time series forecasting, instead of letting it improvise the statistics itself. Five FastMCP servers, one per stage of a forecast's real lifecycle, installable as a single pip install.

pip install "omen-agentic-forecasting[all]"

Status: Alpha, published on PyPI. 161 real tests, and every worked example in the companion book was run against live tool output, not hand-typed. CI (GitHub Actions) runs the full suite plus ruff linting and mypy type-checking on every push and PR. See CHANGELOG.md for version history.

Why This Exists

Time series forecasting is a well-studied statistical discipline, but it's easy to get wrong in ways that look fine until they aren't: skipping a stationarity check, never comparing against a naive baseline, reporting a point forecast with no confidence interval attached. Ask a general-purpose AI model to "forecast this series" and it will typically write plausible-looking pandas/statsmodels code from memory that skips exactly those checks — not because the statistics are hard to name, but because getting them right, correctly, inside freshly improvised code, every single time, is a much harder ask of an LLM than it looks.

Omen's answer is to stop asking an agent to improvise the statistics at all. Every operation here — checking stationarity, backtesting a model against a real holdout, computing a confidence interval, deciding whether a retrained candidate is actually worth redeploying — is a pre-built, tested, typed tool, exposed over the Model Context Protocol (MCP) as a FastMCP server with a companion OpenClaw skill (the reasoning workflow around those tools). An AI agent's job becomes choosing which tool to call next and reasoning about the structured result that comes back, not writing statistics code from scratch. Consequential actions, like redeploying a live model, are gated behind an explicit, code-checked confirmation rather than left to an agent's judgment call; open-ended reasoning, like which model family fits a series' shape, is left to the agent, where it belongs.

The Five Layers

That split shows up as five layers, each covering one stage of a forecast's real lifecycle, from first looking at the data through deciding whether to retrain months later:

• Layer 1 — ts-analyst: explore a series — stationarity, seasonality, anomalies, structural breaks — and recommend a forecasting approach with reasoning attached.
• Layer 2 — ts-forecaster: fit candidate models against a common held-out window, backtest them (optionally across multiple rolling origins), compare them statistically, and recommend one grounded in real error metrics and residual diagnostics.
• Layer 3 — ts-deploy: retrain the chosen model on the full series and produce a real forecast beyond the end of the data, with prediction intervals wherever a model can honestly support one (including gradient-boosted trees, via quantile regression), an automated plausibility check against the series' own history, and an optional weighted ensemble across multiple candidates.
• Layer 4 — ts-monitor: once real observations exist, check whether the deployed forecast is still tracking reality (with a bootstrap confidence interval on its error metrics), detect data drift (with an effect size, not just a bare p-value), and recommend whether to retrain — flagging when that recommendation is itself close to the threshold rather than clear-cut.
• Layer 5 — ts-retrain: when ts-monitor says retrain_now, re-run Layers 1–2 on the updated series and deterministically decide whether the freshly backtested candidate beats what's currently deployed by enough to be worth redeploying, confidence-interval-aware on both sides of that comparison. Never redeploys without an explicit confirmed=True — by default that means stopping for human confirmation, and the optional autonomous-mode alternative is backed by a real, code-checked authorization record, not just a prose contract.

Layers 1–4 also expose 13 plot_* tools between them (Layer 5 deliberately has none — a redeploy verdict has no natural chart) that render a matplotlib figure and return it as an inline image in the same turn, with an optional out_path to also save a PNG to disk. These are strictly supplementary: every plot re-derives its picture from the exact same computation its JSON counterpart already returns, so a chart can never silently disagree with the numbers behind it.

Quick Start

pip install "omen-agentic-forecasting[all]"

That installs every layer's dependencies and five console scripts (ts-analyst-server, ts-forecaster-server, ts-deploy-server, ts-monitor-server, ts-retrain-server), each a FastMCP server ready to register with any MCP-speaking agent client — OpenClaw, Claude Code, Claude Desktop, or similar. A ready-to-merge OpenClaw config snippet, openclaw.config.snippet.jsonc, lives at the repo root. Only need a subset of layers? Per-layer extras exist too: [analyst], [forecaster], [deploy], [monitor], [retrain].

For the full platform-by-platform setup walkthrough — OpenClaw, Claude Code, generic MCP clients, and the single most common installation gotcha (narrated as it actually happened) — see book/chapter-02-installing-omen.md.

Once connected, a single message can drive the whole pipeline:

Use ts-analyst to explore a synthetic time series, then use ts-forecaster to fit and backtest candidate models informed by what you found, then use ts-deploy to produce a 30-day forecast with the best-performing model and settings.

Using It End to End

Once time has passed and the CSV has real new observations:

Use ts-monitor to check whether that forecast is holding up against what actually happened, and tell me if I should retrain.

Right after that first real ts-deploy call, record what got deployed so Layer 5 has a baseline to compare against later:

Use ts-retrain to record that this model and its backtest metrics are now deployed.

If ts-monitor comes back with retrain_now:

Use ts-retrain to re-run analyst and forecaster on the updated series and tell me whether the new candidate is actually worth redeploying.

That call stops at the verdict — ts-retrain never redeploys on its own in the default mode. If the verdict says should_redeploy: true and you want to proceed, confirm it explicitly in a follow-up message:

Go ahead and redeploy the candidate you just recommended.

which is what actually triggers ts-retrain__execute_redeploy(..., confirmed=True) and updates the manifest.

If you'd rather not be asked each time for a specific series, opt into autonomous mode explicitly:

For the daily_demand.csv series specifically, you're authorized to redeploy automatically whenever ts-retrain finds a candidate that beats the current deployment — no need to ask me first. Everything else still needs my confirmation as usual.

That grant is what the agent should turn into a persisted record via ts-retrain__authorize_autonomous_mode(csv_path="daily_demand.csv", authorized_by="user, in conversation") — not just remember for the rest of the session. With that record in place, a later retrain_now cycle for that series will call execute_redeploy(confirmed=True, autonomous=True) itself once should_redeploy: true comes back (which itself re-checks the record before acting), and report what it did rather than pausing to ask.

Learn More

• book/ — Agentic Time Series Forecasting for Supervillains, a complete 22-chapter e-book (plus glossary, tool reference, and further-reading appendices) teaching time series forecasting through this toolkit, one concept per chapter, each worked example run for real against the live MCP servers rather than hand-typed. Start at book/outline.md or jump straight to book/chapter-01-*.md.
• book/appendix-b-tool-reference.md — a one-line-per-tool lookup table across all five layers, if you just want to know what a given tool does without reading the chapter that introduces it.
• AGENTS.md — the project's own dense, internal design log: exact tool mechanics, confirmed edge cases, and non-obvious conventions, written for whoever picks up this code next rather than for a first read.
• blog-posts/ — shorter, punchier write-ups covering the same five layers, one post per layer plus an introductory overview.
• prompts/testing-and-learning-prompts.md — ready-to-use prompts across all five layers for hands-on practice with your own data.

Project Layout

omen/
├── pyproject.toml
├── LICENSE
├── README.md
├── AGENTS.md
├── .gitignore
├── openclaw.config.snippet.jsonc
├── .github/
│   └── workflows/
│       └── ci.yml              # ruff + mypy + pytest (3.10/3.11/3.12) on every push/PR
├── graphify-out/                # knowledge graph of this repo (code + docs + book + images)
│   ├── graph.json              # GraphRAG-ready nodes/edges
│   ├── graph.html              # interactive visualization
│   ├── GRAPH_REPORT.md         # plain-language summary
│   ├── manifest.json
│   └── .graphify_labels.json
├── src/
│   └── omen/
│       ├── __init__.py            # version + skills_dir() helper
│       ├── data_prep.py           # shared: synthetic data + CSV loader (used by all 5 layers)
│       ├── plotting.py            # shared: render_plot() -- inline Image + optional PNG, used by all plot_* tools
│       ├── analyst/
│       │   ├── __init__.py
│       │   ├── analysis_tools.py  # Layer 1 diagnostic functions
│       │   ├── plot_tools.py      # Layer 1 plotting functions (6 tools)
│       │   └── server.py          # FastMCP server: ts-analyst
│       ├── forecaster/
│       │   ├── __init__.py
│       │   ├── model_tools.py     # Layer 2 fit/backtest functions
│       │   ├── plot_tools.py      # Layer 2 plotting functions (3 tools)
│       │   └── server.py          # FastMCP server: ts-forecaster
│       ├── deploy/
│       │   ├── __init__.py
│       │   ├── forecast_tools.py  # Layer 3 retrain/forecast functions
│       │   ├── plot_tools.py      # Layer 3 plotting functions (1 tool)
│       │   └── server.py          # FastMCP server: ts-deploy
│       ├── monitor/
│       │   ├── __init__.py
│       │   ├── monitor_tools.py   # Layer 4 comparison/drift/retrain-decision functions
│       │   ├── plot_tools.py      # Layer 4 plotting functions (3 tools)
│       │   └── server.py          # FastMCP server: ts-monitor
│       ├── retrain/
│       │   ├── __init__.py
│       │   ├── retrain_tools.py   # Layer 5 deployment-manifest + redeploy-decision functions
│       │   └── server.py          # FastMCP server: ts-retrain (no plotting tools -- see below)
│       └── skills/                # bundled as package data -- see skills_dir()
│           ├── ts-analyst/SKILL.md
│           ├── ts-forecaster/SKILL.md
│           ├── ts-deploy/SKILL.md
│           ├── ts-monitor/SKILL.md
│           └── ts-retrain/SKILL.md
├── tests/
│   ├── test_data_prep.py
│   ├── test_analyst_tools.py
│   ├── test_analyst_plot_tools.py
│   ├── test_forecaster_tools.py
│   ├── test_forecaster_plot_tools.py
│   ├── test_deploy_tools.py
│   ├── test_deploy_plot_tools.py
│   ├── test_monitor_tools.py
│   ├── test_monitor_plot_tools.py
│   └── test_retrain_tools.py
├── blog-posts/                    # draft write-ups about this project, not part of the package
│   ├── introducing-omen.md
│   ├── ts-analyst-gets-a-statistics-degree.md
│   ├── ts-forecaster-shows-its-work.md
│   ├── ts-deploy-ships-it.md
│   ├── ts-monitor-learns-to-doubt-good-news.md
│   └── ts-retrain-checks-its-papers.md
├── prompts/                       # ready-to-use prompts for testing/learning each layer
│   └── testing-and-learning-prompts.md
└── book/                          # "Agentic Time Series Forecasting for Supervillains" e-book, not part of the package
    ├── dedication.md, ai_use_statement.md, about_the_author.md, about_the_series.md
    ├── title-page-image.png       # cover image, also embedded above and on the book's title page
    ├── outline.md
    ├── chapter-01-introducing-omen-and-agentic-ai.md ... chapter-22-conclusion.md
    ├── appendix-a-glossary.md, appendix-b-tool-reference.md, appendix-c-further-reading.md
    └── examples/
        ├── README.md                  # how the dataset/plot/assembly scripts fit together
        ├── generate_book_datasets.py  # regenerates every dataset the book uses
        ├── generate_book_plots.py     # regenerates every plot image the book embeds
        ├── assemble_book.py           # concatenates the book into one Markdown file + PDF
        ├── book_datasets/             # committed CSV copy of every dataset the book uses
        └── images/                    # the 32 real PNGs embedded in the book's chapters

Development

python -m venv .venv && source .venv/bin/activate   # optional but recommended
pip install -e ".[all,dev]"    # editable install, every layer + test deps
pytest                          # 161 tests
ruff check .                    # lint
mypy                            # type-check

Sanity-check each server runs standalone (Ctrl+C to stop; no output/crash = good):

ts-analyst-server
ts-forecaster-server
ts-deploy-server
ts-monitor-server
ts-retrain-server

The importable module stays plain omen regardless of install method — only the PyPI listing name is omen-agentic-forecasting (plain omen was already taken; see the comment above [project.scripts] in pyproject.toml). See AGENTS.md for build/release commands and the full set of non-obvious project conventions.

Contributing and Support

Contributions are welcome. See CONTRIBUTING.md for how to report bugs and submit changes, and SUPPORT.md for how to get help, what response times to expect, and how the project is governed. Everyone taking part is expected to follow the Code of Conduct.