---
license: mit
language:
- en
tags:
- time-series
- time-series-decomposition
- benchmark
- component-recovery
- symbolic-regression
- icml-2026
pretty_name: TSDecompose Benchmark
size_categories:
- 1K<n<10K
task_categories:
- tabular-regression
- time-series-forecasting
---

# TSDecompose Benchmark

This repository is the release bundle for **Time-Series Decomposition as a Standalone Task: A Mechanism-Identifiable Benchmark** (ICML 2026 camera-ready artifact).

It combines:

- a paper-aligned benchmark data release;
- a post-rebuttal second expansion of the real-data evidence;
- a source snapshot of `TSDecompose` / `tsdecomp`;
- machine-readable result tables for the synthetic benchmark, real-data proxy track, and semi-synthetic transfer checks.

This is a repository-style artifact, not a PyPI package release. The code is included so users can inspect and run the benchmark from source.

Public Hugging Face links:

- Dataset repository: [Zipeng365/TSDecompose-Benchmark](https://huggingface.co/datasets/Zipeng365/TSDecompose-Benchmark)
- Web leaderboard: [Zipeng365/TSDecompose-Benchmark-Leaderboard](https://huggingface.co/spaces/Zipeng365/TSDecompose-Benchmark-Leaderboard)

## What Is Included

```text
data/paper_tables/
  Exact CSV versions of the camera-ready main and appendix tables, plus the
  raw 6-scenario x 50-draw six-family leaderboard used to regenerate Table 2.
  Appendix robustness/transfer tables are paper snapshots; not all of them
  are regenerated by the lightweight paper-core runner.

data/paper_figures/
  Paper figure assets used by the leaderboard Space for visual alignment checks.

data/synthetic_full22_extension/
  Benchmark-only 6-scenario synthetic extension with a 22-method roster.

data/real_proxy22/
  Real-data companion track with canonicalized public time series and proxy diagnostics.

data/semisynth_transfer/
  Semi-synthetic transfer summary and raw metric exports, excluding large downloaded source files.

data/post_rebuttal_second_expansion/
  Later real-data expansion from the rebuttal stage, separated from the paper-aligned tables.

code/TSDecompose/
  Source snapshot for the decomposition benchmark API and CLI. The `tsdecomp`
  Python package source is included at `code/TSDecompose/src/tsdecomp/`.
  The paper core benchmark runner is
  `code/TSDecompose/scripts/run_paper_benchmark.py`.

site_data/v1.0.0/
  Lightweight data files for a future leaderboard website or Hugging Face Space,
  regenerated from the current paper-aligned outputs.

metadata/
  Release manifest, schema notes, table provenance notes, file inventory, and
  checksums.
```

## Paper-Aligned Scope

The primary paper studies standalone decomposition as component recovery under controlled synthetic mechanisms. The main leaderboard and paper tables should be read as diagnostic capability profiles, not as a universal single-score ranking.

The 22-method files are included as benchmark-only expansion and transfer tracks. Several rows correspond to additional method prototypes or benchmark-side mechanism proxies, not camera-ready paper claims. These rows use the same 6-scenario, 50-draw synthetic protocol where applicable, but they should not replace the primary six-family Table 2 / Figure 3 interpretation.

The one-command reproduction path in this repository regenerates the primary
paper-core synthetic leaderboard. Appendix robustness and transfer checks are
released as exact camera-ready CSV summaries, with source summaries and run
metadata where available. They should be used to audit the paper tables, but
they are not all exposed through the same lightweight runner.

## Versioned Evidence Layout

The release separates the frozen camera-ready paper snapshot from benchmark extensions:

- Camera-ready paper snapshot: `data/paper_tables/` and `data/paper_figures/`.
- Living benchmark extensions: `data/synthetic_full22_extension/`, `data/real_proxy22/`, and `data/semisynth_transfer/`.
- Post-rebuttal second expansion: `data/post_rebuttal_second_expansion/`.

The second expansion includes the real-data additions used in the rebuttal: mechanism-aware checks on CO2 and tides, plus a broader six-dataset proxy and stability panel. It is included for transparency and follow-up analysis, not to replace the camera-ready paper tables.

## Core Task

Given an observed time series `y`, a decomposition method returns components:

```text
(trend, seasonal, residual)
```

The synthetic benchmark evaluates recovery against known ground-truth components using:

- trend R2;
- trend DTW;
- seasonal R2;
- seasonal spectral correlation;
- seasonal max-lag correlation.

These five metrics are the paper's core component-recovery metrics. They are
computed per generated draw after method outputs are aligned to `(trend,
seasonal, residual)`, then averaged by scenario, tier, or regime. Coverage
reports the fraction of successful runs with valid metrics.

The camera-ready Table 2 view intentionally displays only two of the five core
metrics: Trend R2 and Seasonal spectral correlation, each split over stationary
regimes (Tiers 1-2) and non-stationary regimes (Tier 3). Its displayed values
use a tier-balanced aggregation: first compute method means separately for Tier
1, Tier 2, and Tier 3 over valid metric values; stationary columns are the
equal-weight average of Tier 1 and Tier 2 means, while non-stationary columns
are Tier 3 means. Seasonal metrics are undefined for the trend-only scenario,
so the by-tier file records metric-specific valid-row counts. Figure 3 should
be read as the five-metric capability profile. The expanded 22-method files may
include mean-rank convenience columns, but those are extension summaries and
are not the primary paper definition.

Machine-readable metric definitions are in:

```text
site_data/v1.0.0/evaluation_metrics.json
```

## Data Tracks

### `paper_tables`

Small CSV files matching the camera-ready manuscript tables. These are the safest files to cite directly when checking paper consistency. The paper-core reproduction files are:

- `global_performance_summary.csv`: the rounded Table 2 values used by the manuscript and leaderboard.
- `paper_core_50draw_leaderboard.csv`: raw 1,800-row paper-core output (6 scenarios x 50 draws x 6 methods).
- `paper_core_50draw_by_tier.csv`: tier-level means and metric-specific valid-row counts used to derive the tier-balanced Table 2 columns.

Appendix table snapshots include bounded tuning, period robustness, alignment
robustness, boundary sensitivity, real-data proxy, semi-synthetic transfer, and
MSSA pilot summaries. Their audit status is summarized in
`metadata/paper_table_provenance.md`.

### `synthetic_full22_extension`

This benchmark-only track uses the same 6-scenario synthetic generator and five-metric evaluation protocol with length 512, 50 draws per scenario, true-period-given evaluation, and a 22-method roster. It includes raw rows, overall summaries, by-scenario summaries, by-tier summaries, coverage, protocol matrix, and backend-selection metadata. It is an expanded roster view, while the camera-ready Table 2 / Figure 3 source is the six-family paper table under `data/paper_tables/`.

### `real_proxy22`

This track uses public real time series with known periods, known mechanisms, or motivated trend expectations. Because real data rarely expose exact component ground truth, the metrics are proxy diagnostics: band plausibility, resampling stability, spectral overlap, residual autocorrelation, trend smoothness, and reconstruction error.

### `semisynth_transfer`

This benchmark-only transfer track injects known mechanisms into six real monthly backgrounds using a 22-method roster, three mechanisms, two background scales, and eight windows per setting. The released five-metric ranking is `data/semisynth_transfer/results/summary/ranking_paper_5metric_overall.csv`. It is a living benchmark extension rather than a camera-ready paper table. Large downloaded source files are intentionally excluded from this release; the canonical small CSV backgrounds and metric tables are included.

The camera-ready paper's semi-synthetic paragraph is the frozen six-family
snapshot in `data/paper_tables/semisynthetic_transfer_summary.csv`; the
22-method transfer files in this directory are a later benchmark extension and
may have different method-level ordering.

### `post_rebuttal_second_expansion`

This later companion track contains rebuttal-stage real-data evidence. `real_physics_track_b` covers CO2 and tides with mechanism-informed approximate structure. `real_proxy_track_c` covers CH4, GPCC precipitation, NDVI, QBO, Arctic sea ice, and sunspots using proxy and stability diagnostics.

## Source Snapshot

The source tree is under:

```text
code/TSDecompose/src/
```

The standalone `tsdecomp` package source is included directly in this Hugging
Face repository:

```text
code/TSDecompose/src/tsdecomp/
```

Compiled development-machine binaries are intentionally excluded; the package
uses the pure-Python fallback path unless users build their own native extension.

To run from source:

```bash
cd code/TSDecompose
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
export PYTHONPATH="$PWD/src"
python scripts/run_paper_benchmark.py
```

On Windows PowerShell:

```powershell
cd code/TSDecompose
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
$env:PYTHONPATH = "$PWD\src"
python scripts/run_paper_benchmark.py
```

This command runs the camera-ready core synthetic benchmark: 6 scenarios,
50 deterministic draws per scenario, and therefore 300 generated synthetic
series. The default method set is the six-family Table 2 roster
(`ma_baseline,stl,ssa,emd,vmd,wavelet`). Because each generated series is
evaluated by each selected decomposition method, the raw `leaderboard.csv` has
one row per scenario, draw, seed, and method.
The public `data/paper_tables/paper_core_50draw_leaderboard.csv` file is this
raw output after stable row sorting; `paper_core_50draw_by_tier.csv` and
`global_performance_summary.csv` are deterministic aggregations of it.
The script pins imports to the bundled source snapshot, so local editable
installs of other `tsdecomp` versions will not change the run.

Minimal smoke run:

```bash
python scripts/run_paper_benchmark.py --smoke
```

Equivalent direct CLI call for the full paper core run:

```bash
python -m tsdecomp run_leaderboard --suite core --methods ma_baseline,stl,ssa,emd,vmd,wavelet --seeds 0 --n_samples 50 --length 512 --dt 1.0 --out artifacts/paper_core_benchmark --aggregate
```

The script entrypoint is recommended for reproduction; the direct module CLI is
included for clean environments where this source tree is the resolved
`tsdecomp` package.

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`.

## Suggested Citation

```bibtex
@inproceedings{wu2026tsdecompose,
  title = {Time-Series Decomposition as a Standalone Task: A Mechanism-Identifiable Benchmark},
  author = {Wu, Zipeng and Wei, Jiani and Zhou, Shiqiao and Chen, Jiajun and Spill, Fabian and Andrews, James W.},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  year = {2026}
}
```

## License

The release bundle is provided under the MIT License. External real-data sources retain their original terms; see `data/real_proxy22/results/summary/dataset_discovery_log.md` and the registry files for provenance notes.