code/TSDecompose/README.md

TSDecompose Source Snapshot

This folder contains the source snapshot shipped with the benchmark release.

It is intentionally included as repository source code rather than as a PyPI-style package release. The import name remains tsdecomp for compatibility with the benchmark scripts.

Layout

src/tsdecomp/
  Unified decomposition API, method registry, CLI, metrics, and leaderboard runner.

src/synthetic_ts_bench/
  Synthetic scenario generator and component-recovery utilities.

scripts/run_paper_benchmark.py
  One-command runner for the paper-aligned core benchmark.

requirements.txt
  Runtime dependencies for local execution.

One-Command Paper Benchmark

Install dependencies from this folder, then run the paper core benchmark:

pip install -r requirements.txt
export PYTHONPATH="$PWD/src"
python scripts/run_paper_benchmark.py

The script pins imports to this source snapshot, so it is safe to use even if a different tsdecomp package is installed elsewhere on the same machine.

Windows PowerShell:

pip install -r requirements.txt
$env:PYTHONPATH = "$PWD\src"
python scripts/run_paper_benchmark.py

The default command runs the paper-aligned core synthetic benchmark:

• 6 scenarios;
• 50 deterministic generated draws per scenario;
• 300 generated synthetic series total;
• the camera-ready six-family Table 2 roster (ma_baseline, stl, ssa, emd, vmd, wavelet).

The raw result table is written to:

artifacts/paper_core_benchmark/leaderboard.csv

In the dataset release, this same paper-core raw output is stored as data/paper_tables/paper_core_50draw_leaderboard.csv after stable row sorting. The manuscript Table 2 values are derived from data/paper_tables/paper_core_50draw_by_tier.csv using the tier-balanced rule: stationary columns average Tier 1 and Tier 2 means with equal weight, while non-stationary columns use Tier 3 means. The by-tier file records metric-specific valid-row counts because seasonal metrics are undefined for the trend-only scenario.

Aggregated summaries are written under:

artifacts/paper_core_benchmark/summary/

For a quick dependency and integration check:

python scripts/run_paper_benchmark.py --smoke

This smoke run keeps the same 6 paper scenarios but uses 1 draw per scenario and the smaller stl,wavelet method subset.

Optional Direct CLI

python -m tsdecomp validate --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet

Windows PowerShell:

python -m tsdecomp validate --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet

Use the script entrypoint above for reproduction. The direct module CLI is shown only for clean environments where no other editable tsdecomp install can shadow this source tree.

The native extension binary from the development machine is not included. The release uses the pure-Python fallback path by default. To test a separately built native extension, set TSDECOMP_ALLOW_EXTERNAL_NATIVE=1 before importing tsdecomp.