张宗平
e2c0756a78
- generate_series: baseline + trend + seasonality + noise (opt heavy-tail)
- inject_anomaly: spike / level_shift / variance_change / missing_segment / drift
* non-overlapping segments (TDD caught an overlap bug) -> labels exactly
match contiguous [start,end) runs
* returns (series, point labels, segment metadata)
- couple_event: persistent per-channel step at t + event text
- add_missing: NaN drop at given rate
- tests/test_synthesis.py (14 tests, RED->GREEN; fixed overlap regression)
Exit criteria: seed-deterministic; labels cover segments; event step persistent.
tsmm
Time-series as a Modality — an experimental, single-GPU multimodal model that treats multivariate time series as an independent modality, jointly injected into a small LLM (Qwen2.5-0.5B) for time-series question answering and reasoning (ChatTS route).
Status
M1 (data pipeline) under construction. See
docs/superpowers/specs/2026-06-29-ts-as-modality-design.md for the design and
docs/superpowers/plans/2026-06-29-ts-as-modality-plan.md for the task plan.
Layout
src/tsmm/ package (data, model, train, eval)
configs/ training / data configs
scripts/ offline generation & training entrypoints
tests/ pytest suite
data/ generated datasets (gitignored)
checkpoints/ model checkpoints (gitignored)
Install (dev)
python3 -m venv .venv
. .venv/bin/activate
pip install -e . # full deps (incl. torch/transformers/peft/...)
# M1-only lightweight install:
pip install numpy tqdm pyyaml pytest && pip install -e . --no-deps