Card: synthetic data — what is there and how to load it

README.md

@@ -2,7 +2,9 @@
 license: other
 license_name: per-dataset
 license_link: https://huggingface.co/datasets/zyplai/MarathonTS/blob/main/LICENSES.md
-pretty_name: MarathonTS
+pretty_name: 'MarathonTS: synthetic long time series and benchmark results'
+task_categories:
+- time-series-forecasting
 tags:
 - time-series
 - synthetic-data
@@ -12,7 +14,7 @@ size_categories:
 - 10K<n<100K
 ---
 
-# MarathonTS — benchmark artifacts
+# MarathonTS — synthetic long time series and benchmark results
 
 MarathonTS is a benchmark of long synthetic time series. It compares the assembly of a long series from a pool of
 generated windows (which windows, in what order, how the seams are processed) with long-series generators,
@@ -20,8 +22,10 @@ including pretrained forecasting models, on 9 open hourly datasets with 10 seeds
 fidelity to the real data (83 metrics against train, validation and an out-of-time split), its utility (TSTR
 against TRTR in 14 classification and regression tasks), memorisation and cost.
 
-This dataset holds the artifacts of the two published runs: the series, pools, generator weights, per-unit
-metrics, utility predictions, run records, the collected tables and the reports.
+This dataset holds **3,220 synthetic long hourly series and 63,000 synthetic windows** generated from seven open
+datasets by 30 methods — window assembly from generated pools and long-series generators — together with
+everything measured on them: per-series fidelity metrics, utility predictions, run records, the collected
+tables, the rankings and the reports of the two published runs.
 
 | Resource | Link |
 |---|---|
@@ -31,6 +35,53 @@ metrics, utility predictions, run records, the collected tables and the reports.
 | Documentation | https://zypl-ai.github.io/MarathonTS |
 | Leaderboard | https://huggingface.co/spaces/zyplai/MarathonTS |
 
+## Synthetic data
+
+| What | Folder | Size | Format |
+|---|---|---|---|
+| **Long series** — assembled from windows (stitched) or generated directly | `<run>/series/<dataset>/<method>/seed<k>.parquet` | 2,100 (`run_h200`) + 1,120 (`run_l4`) series of 5,040 hours (30 weeks) | one row per hour, real units |
+| **Window pools** — generated windows before assembly (not stitched) | `<run>/pools/<dataset>/<generator>/seed<k>.parquet` | 140 pools (`run_h200`: VAE and zEDGE) + 70 (`run_l4`: VAE), 300 windows of 168 hours each | array `(300, 168, 3)` in `[0, 1]` |
+
+Datasets: `beijing_pm25`, `bike`, `bitcoin`, `electricity`, `household`, `jena`, `metro` (the synthetic data of ETTh1
+and PJM are withheld for licence reasons, see below), 10 seeds each.
+
+**Long series.** Columns: `t` (hour, 0 … 5039), `value` (the synthetic series, in the units of the real one),
+`how` (hour of week, 0–167), `window` (index of the pool window the point comes from; −1 for a long-series
+generator), `piece` (consecutive piece of the series), `seam` (true at the first point after a seam). The method
+name tells how the series was made:
+
+- `vae_<assembler>`, `zedge_<assembler>` — assembled from the pool of the VAE or of zEDGE by an assembler
+  (`rand` random, `greedy`, `atsp`, `pctsp`, `mbb`, `knn`, `quilting`, `herding`, `zseries`), and
+  `<pool>_zseries_{xfade,wsola,quilt}` — the same path with the seams processed;
+- `diffusion_ts`, `chronos`, `timesfm`, `timestral`, `zedge_ts_long`, `zedge_ts_ar` — long-series generators
+  (no pool; the seams are the boundaries of their generation chunks).
+
+**Window pools.** Channel 0 is the value scaled by min-max on the training split, channels 1–2 the hour of week
+as `(sin + 1) / 2` and `(cos + 1) / 2`. Back to real units: `value = lo + x · (hi − lo)` with `lo`, `hi` of the
+dataset in `<run>/tables/datasets.parquet`.
+
+```python
+import json
+import numpy as np
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+repo = dict(repo_id="zyplai/MarathonTS", repo_type="dataset")
+
+# one long synthetic series (stitched from zEDGE windows by image quilting)
+series = pd.read_parquet(hf_hub_download(filename="run_h200/series/bike/zedge_quilting/seed0.parquet", **repo))
+
+# one pool of 300 generated windows (not stitched), back to real units
+row = pd.read_parquet(hf_hub_download(filename="run_h200/pools/bike/tsgm_vae/seed0.parquet", **repo)).iloc[0]
+pool = np.frombuffer(row["data"], dtype=row["dtype"]).reshape(json.loads(row["shape"]))      # (300, 168, 3)
+ds = pd.read_parquet(hf_hub_download(filename="run_h200/tables/datasets.parquet", **repo)).set_index("name")
+windows = ds.at["bike", "lo"] + pool[:, :, 0] * (ds.at["bike", "hi"] - ds.at["bike", "lo"])  # (300, 168)
+```
+
+Every synthetic series has its fidelity metrics in `fidelity/` and its utility in `utility/` and
+`tables/utility.parquet`, so a series can be chosen by quality. All series of one folder:
+`snapshot_download(**repo, allow_patterns=["run_h200/series/**"], local_dir="marathonts")`.
+
 ## Two runs
 
 | | `run_h200/` — main run | `run_l4/` — replication run |