sima — simultaneous VARMA on the ATSW ladder

sima is the MCP assistant for systems of time series: VARMA models by exact maximum likelihood, built on the univariate models of each series. The engine is drvarma, whose ladder mode takes fue's univariate files as input. This package is the assistant: the protocol the model walks, the evidence at each node, and the menu of decisions.

It is the third rung of the ATSW ladder:

art (one series)  →  mtram (transfer networks)  →  sima (systems)

You arrive at sima with several univariate models (.pre files from art or fue), or from mtram when its network identification found a cycle: two series that feed each other, which no transfer network can hold. sima takes the same files.

Install

pip install sima-tseries            # from univariate models (.pre / .inp files)
pip install "sima-tseries[raw]"     # also from raw data: adds art-tseries, pandas, openpyxl

The raw entry (load_data, characterize, build_univariate, write_specs) uses art's engine, so it needs the raw extra; so do the worked examples that start from the data (lutkepohl_poskitt, tiao_box).

Register the server in your MCP client (the command is sima, stdio). The binary wheels of drvarma carry the compiled likelihood the ladder needs. If they are missing, drvarma warns and runs about 250 times slower.

The protocol

Two rules shape everything:

• The univariate model is the yardstick. A VARMA that does not forecast better than the univariate models out of sample has no reason to exist, however significant its cross terms are in sample.
• Evidence and a menu, not a verdict. The tools show the evidence and the options with their arguments for and against. The analyst decides in the guided lane, and the model decides, in writing, in the autonomous lane.

See docs/DESIGN.md and the generated tool reference docs/TOOLS.md.

User manual

• Jenkins and Alavi's method: the method of Jenkins and Alavi (1981), how sima implements it tool by tool, and their muskrat-mink example worked through from art's univariate models — runnable in examples/jenkins_alavi/.
• Tiao and Box's method: the vector first, Tiao and Box (1981) and Box and Tiao (1977): the stepwise M(l), the canonical analysis of the levels, the structure and the simplification. Their gas furnace is runnable in examples/tiao_box/, and the flour prices in examples/flour_prices/.
• The echelon form: Lütkepohl and Poskitt's (1996) identified VARMA: the Kronecker indices from the data (identify_echelon), the fit (estimate(indices=...)), and their flour prices worked through, runnable in examples/lutkepohl_poskitt/.

Each worked example runs node by node in a conversation with load_example(<name>) (examples lists them).

Licence

GPL-2.0-or-later. Authors: A.B. Treadway, J.A. Mauricio and D.E. Guerrero (the engine); D.E. Guerrero (the assistant).