TSFM-PEFT-Bench

A cross-architecture benchmark for evaluating Parameter-Efficient Fine-Tuning (PEFT) recommendation reliability in Time Series Foundation Models (TSFMs). Companion code and artifacts for the paper "TSFM-PEFT-Bench: A Cross-Architecture Benchmark for PEFT Selection in Time Series Foundation Models" (under double-blind review at NeurIPS 2026 Datasets and Benchmarks Track).

Quick metadata:

• License: Apache-2.0 (LICENSE)
• Croissant manifest: tsfm_peft_bench.croissant.json (MLCommons Croissant 1.0 with mandatory RAI fields)
• Headline scale: 882 paper-included primary-model runs (3 architectures × 4 domains × 6 main methods + rank/locus sweeps)
• Reproduction in one command: python scripts/reproduce_paper_tables.py
• Code repository (anonymous review): https://anonymous.4open.science/r/tsfm-peft-bench
• Dataset (Hugging Face): EvalData/tsfm-peft-bench — 972 run records + Croissant 1.0

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Hosting and accessibility (NeurIPS 2026 D&B Track)

Per the NeurIPS 2026 Evaluations & Datasets hosting policy, this artifact will be hosted at:

Asset	Platform	Notes
Code (frozen at submission)	Anonymous-4-Open-Science (review) → Hugging Face Spaces (camera-ready)	All scripts, configs, src/
Run manifest + headline numbers	Hugging Face Datasets tsfm-peft-bench/runs	paper_manifest.json, paper_numbers.{json,tex}, selector_evaluation.json
Per-run JSON files (full grid)	Hugging Face Datasets tsfm-peft-bench/runs	results/expansion/{domain,rank,locus}/*.json (~50–200 MB total)
Domain shift profiles	Hugging Face Datasets tsfm-peft-bench/runs	domain_shift_profiles.json
Croissant metadata	top-level tsfm_peft_bench.croissant.json	Auto-validated against MLCommons spec 1.0

The Croissant file is the canonical machine-readable description of the benchmark. RAI fields (limitations, biases, sensitive information, intended use, social impact, sources, preprocessing, release plan) are populated.

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What's in here

• src/ — model wrappers (Chronos, MOMENT, Moirai, TimesFM), PEFT adaptations (LoRA / DoRA / IA³ / Adapter / Prefix / Head-only / Full-FT), dataset loaders, and evaluation utilities.
• scripts/ — Hydra-based single-run trainer (train.py), full benchmark driver (run_expansion.py), analysis pipeline (analyze_expansion_v2.py, reproduce_paper_tables.py), selector (build_selector.py), and mechanism probes (subspace_probe.py, gradient_probe.py).
• configs/ — Hydra YAML configs for models, adaptations, and data; zero hard-coded hyperparameters.
• tests/ — pytest suite (100 tests) covering data loaders, adaptations, metrics, shift profiles, and analysis utilities.
• results/ — paper artifacts (manifest, ANOVA, selector tables, paper numbers). paper_manifest.json is the single source of truth for paper-included runs.
• paper_submission.tex, paper_appendix.tex, paper_supplementary.tex, neurips_checklist.tex — the manuscript and supplementary materials.

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Setup

The repository targets Python 3.10–3.12.

# Recommended (exact reproduction): pin every transitive dep
python -m pip install -r requirements-lock.txt
python -m pip install -e .

# Lighter (looser bounds): top-level constraints only
python -m pip install -e ".[dev]"

requirements-lock.txt is captured by pip freeze from the environment that produced the released results (PyTorch 2.11, chronos-forecasting 2.2.2, uni2ts 2.0.0, momentfm @ upstream commit 38f7310a).

TimesFM is optional and conflicts with Python 3.12 (paxml/lingvo dependencies). Install only on Python 3.10/3.11:

python -m pip install -e ".[timesfm]"

GPU: experiments were run on 4× RTX 3090 (cluster) and 1–4× RTX 3060 nodes. Mixed-precision (FP16/BF16) is always enabled; FP32 is unsupported.

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Reproducing the paper

Headline tables

# Re-derive paper_manifest.json + paper_numbers.{json,tex} from raw runs
python scripts/reproduce_paper_tables.py \
    --input_dir results/expansion --output_dir results

# ANOVA / outlier filtering / per-architecture statistics (v2)
python scripts/analyze_expansion_v2.py

# Selector evaluation (LOOCV, 12 held-out cells)
python scripts/build_selector.py

These three commands regenerate every numerical claim in paper_submission.tex from raw run JSON. Latex macros for the headline numbers are emitted to results/paper_numbers.tex.

Single-run training

python scripts/train.py model=chronos adaptation=lora data=ett_m1
python scripts/train.py model=chronos adaptation=lora data=ett_h1 \
    adaptation.rank=16 training.lr=1e-4 training.epochs=50

Full benchmark grid

# 3 primary models × 4 domains × 7 main methods × 5 seeds = 882 paper-included runs
python scripts/run_expansion.py \
    --models chronos,moment,moirai \
    --mode domain \
    --seeds 42,123,7,2024,3407 \
    --save_checkpoints \
    --checkpoint_dir checkpoints/expansion

scripts/run_benchmark.sh wraps the full sweep across the three modes (domain / rank / locus).

Evaluation from a checkpoint

python scripts/evaluate.py \
    --checkpoint checkpoints/expansion/chronos/<experiment_id>.pt \
    --model chronos --data ett_m1

Both train.py and run_expansion.py save checkpoints in a unified schema (backbone_state_dict + adaptation_method + adaptation_config + prediction_length / context_length). evaluate.py accepts either backbone_state_dict or the legacy state_dict key for backward compatibility with pre-2026-04-27 checkpoints.

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Repository conventions

• Docstrings and error messages are written in Korean; identifiers and paper-facing artifacts are in English. See CLAUDE.md for full coding standards.
• All hyperparameters live in configs/; do not hard-code values in scripts.
• External libraries (chronos, peft, transformers, wandb) are imported dynamically via importlib and accessed through Protocol types in src/. Direct imports in scripts/ and tests/ are fine.
• from __future__ import annotations at the top of every module.

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Data and checkpoints

data/ is .gitignored. Public datasets used:

• ETTm1: standard ETT benchmark (Zhou et al., 2021).
• Exchange-rate ("Finance"): Lai et al., 2017.
• SMD: Server Machine Dataset (Su et al., 2019), entity boundaries preserved during splitting (src/data/smd.py).
• PhysioNet: subset processed in src/data/physionet.py, subject IDs preserved across splits to prevent leakage.

checkpoints/ is a local symlink to NAS storage on the maintainer's machine. Downstream users should either remove the symlink and create a local directory, or override the path with checkpoint_dir=... / --checkpoint_dir <path> on the command line.

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Layout

src/
├── adaptation/    # LoRA / DoRA / IA3 / Adapter / Prefix / Head / Full
├── data/          # ETT, finance, SMD, PhysioNet, shift metrics
├── evaluation/    # MAE / MSE / MASE / CRPS / CKA
├── models/        # Chronos / MOMENT / Moirai / TimesFM wrappers
└── utils/         # Seeds, device, logging
scripts/
├── train.py                       # single-run Hydra entry point
├── run_expansion.py               # full benchmark grid
├── reproduce_paper_tables.py      # SoT manifest + paper_numbers regenerator
├── analyze_expansion_v2.py        # ANOVA / outlier policy
├── build_selector.py              # selector LOOCV evaluation
├── subspace_probe.py              # mechanism probe (representation)
└── gradient_probe.py              # mechanism probe (gradient flow)
configs/
├── model/        chronos.yaml | moment.yaml | moirai.yaml | timesfm.yaml
├── adaptation/   lora | dora | ia3 | adapter | prefix | head_only | full_ft
└── data/         ett_m1 | ett_h1 | finance | smd | physionet | ...
results/
├── paper_manifest.json            # SoT: 882 paper-included primary-model runs
├── paper_numbers.{json,tex}       # auto-generated latex macros
├── selector_evaluation.json       # selector LOOCV results
├── expansion_analysis_canonical/  # canonical ANOVA outputs
└── expansion_analysis_v3/         # v3 (current) analysis outputs

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Quality gates

# Tests (100 tests, ~3s on CPU)
pytest tests/ -v

# Lint / format
ruff check src/ scripts/ tests/
black --check src/ scripts/ tests/
isort --check-only src/ scripts/ tests/
mypy src/

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Citation

Anonymous under review. Citation will be added on publication.

License

Apache 2.0 (see LICENSE once added).