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.

License

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

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