ATSW — Box-Jenkins-Treadway time series suite

atsw is an umbrella package: installing it pulls the complete Box-Jenkins- Treadway time series suite plus the MCP server, in one step.

pip install atsw

It installs the estimation engines, the graphics, and three MCP assistants. Requires Python ≥ 3.10. Every engine has a C implementation with an automatic pure-Python fallback, so the suite installs everywhere.

The three assistants, and when to move between them

They are separate servers on purpose. An MCP client sees every connected server at once, so this is not three tools you have to choose between — it is one ladder with three rungs, and each assistant knows when to hand over.

mtram starts from the .pre files art writes, so the univariate rung is already climbed and committed to a file you can read. The handoff to sima is testable, not a preference: if the proposed network contains a CYCLE there is no topological order, the system cannot be written as a triangular VARMA, and it is therefore simultaneous. mtram says so and stops.

Use with an LLM (recommended)

claude mcp add art   -- art-mcp     # one series
claude mcp add mtram -- mtram       # transfer functions and networks
claude mcp add sima  -- sima        # simultaneous VARMA

Then ask Claude to analyse a series (attach a CSV/Excel, or point to an .inp). ART offers a guided workflow (analyst decides, Claude advises step by step, with graphs and your confirmation at each decision) or an autonomous one (Claude/heuristic decides every step and presents a final model). The suite supplies the evidence — graphs, tests, numbers; you and/or Claude supply the criterion at each Box-Jenkins decision node.

Use as a plain Python library (no Claude needed)

import fue
from art.describe import describe_boxcox, describe_identification

ts, _ = fue.inp.load("series.inp")
print(describe_boxcox(ts).summary)          # Box-Cox transformation analysis
print(describe_identification(ts).summary)  # ACF/PACF identification

Estimation and forecasting (FUF) live in fue; the fuf command forecasts from an estimated model.

Background — a modern Box-Jenkins-Treadway

The Box-Jenkins analysis was tremendously popular at its launch as a process for building ARMA models (with extensions). The models themselves are simple, but the iterative building process is a case of false simplicity: in practice the method worked wonderfully if you were Box, Jenkins, or one of their disciples. The real obstacle is training the analyst to make the decisions the process demands — decisions often guided by heuristics.

ATSW combines that criterion with statistical methods to build the models in a modern form: AI — with its limitations — supplies the criterion and the suggestions a trained time series analyst would offer.

The analysis presented here is not the canonical Box-Jenkins, but the extended version of Arthur B. Treadway (a disciple of Gwilym Jenkins), which adds elements and heuristics drawn from his experience producing the Forecasting and Monitoring Services (SPS) of the Spanish economy.

Forecasting is one of the goals of building an ARMAX model — and perhaps an unbeatable one — but univariate analysis is also the foundation of more sophisticated relational analysis. These univariate forecasting models should be the measuring stick for more complex ones: if you cannot beat their forecasts, your model has a problem and you should rethink it.

Components on PyPI

Each component is also installable on its own — atsw just fixes a compatible set: fue · pyfug · art-tseries. See art-tseries's AGENTS.md, docs/QUICKSTART.md, docs/TOOLS.md and docs/ARCHITECTURE.md for the full design, the operating guide and the evidence-vs-criterion philosophy.

License

GPL-2.0-or-later. © David E. Guerrero.