ART — A Real-Time Time-Series Analysis toolkit + MCP server

art-tseries (ART) builds univariate time series models following the Box-Jenkins-Treadway methodology: an iterative, decision-driven process that uses graphical tools and formal tests to identify, estimate, diagnose and refine a model until it is adequate and parsimonious.

ART is the orchestration layer of a four-part suite:

Package	Role
fue	Exact maximum-likelihood estimation (ARMAX + transfer functions) and FUF forecasting. C engine with a pure-Python fallback.
pyfug	High-definition graphics for time series analysis.
ART (`art-tseries`)	Identification, model building, diagnosis, formal tests, versioning — and an MCP server that exposes all of this to an LLM.

The Box-Jenkins-Treadway loop needs judgement at each decision node. ART supplies the evidence (graphs, tests, numbers); a human analyst and/or Claude supply the criterion. Two modes:

Guided — analyst + Claude: Claude proposes with arguments, the analyst decides.
Autonomous — Claude/heuristic decides every step and presents a final model.

Install

pip install art-tseries          # pulls fue + pyfug automatically

This installs the art-mcp command (the MCP server).

Use as an MCP server (Claude Code, etc.)

claude mcp add art -- art-mcp

Then ask Claude to analyse a series. ART will ask whether you want a guided or autonomous analysis and drive the workflow from there.

Use as a library

import fue
from art.describe import describe_boxcox, describe_identification, model_equation

ts, _ = fue.inp.load("series.inp")
print(describe_boxcox(ts).summary)

Methodology

The model-building process is iterative and sequential: each estimation starts from the previous likelihood optimum (the .pre of the previous model), and every step produces a .pre (estimated parameters as initial values) and a .out (results), mirroring fue. Decisions and changes are recorded in a guion.json audit trail. See docs/ARCHITECTURE.md for the full design and the evidence-vs-criterion philosophy.

Documentation

PyPI renders this page only, so everything below is linked absolutely and also ships inside the source distribution — pip download art-tseries --no-binary :all: — so it reads without network.


Quickstart	install, first analysis, EN / ES
MCP tool reference	every `art-mcp` tool, generated from the docstrings
Architecture	how ART, FUE, FUG and FUF fit together
Rescaling	why the scale factor exists and where it acts
Changelog	what changed, version by version

The suite as a whole installs with pip install atsw.

License

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

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