README.md

---
pretty_name: HEPA Corpus (open, license-clean time-series pretraining corpus)
license: other
license_name: mixed-open-per-source-see-provenance-table
license_link: https://huggingface.co/datasets/Forgis/hepa-corpus/blob/main/license_audit_v1.md
task_categories:
- time-series-forecasting
tags:
- time-series
- pretraining
- foundation-model
- event-forecasting
- anomaly-detection
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*/train-*.parquet
  - split: validation
    path: data/*/val-*.parquet
---

# `Forgis/hepa-corpus` — an open, license-clean, streamable time-series pretraining corpus

**28 sources · 95 shards · 567,637 records ·
4.58 B channel-timesteps · 13.8 GB on the Hub.**

Raw, un-patched, un-windowed, **un-normalized** continuous `float32` sequences of shape `(C, T)`,
with per-record provenance, licensing, entity ids and event-label parameters. Built for
HEPA-style JEPA pretraining, but nothing here is HEPA-specific.

> **Every byte in this repo passed a per-source redistribution audit**
> ([`license_audit_v1.md`](license_audit_v1.md)). Sources that did not clear it are **absent
> entirely** — not raw, not derived, not aggregated — and are listed below with the reason.

## Quickstart (streaming — no local download)

```python
from datasets import load_dataset
import numpy as np

ds = load_dataset("Forgis/hepa-corpus", split="train", streaming=True)
r = next(iter(ds))
x = np.asarray(r["values"], dtype=np.float32)        # (C, T) raw, un-normalized
print(r["series_id"], r["source"], x.shape, r["freq"], r["entity_id"])
```

Filter without downloading — every selection is a **query over tags**, never a physical copy:

```python
energy = ds.filter(lambda r: r["domain"] == "energy")
long   = ds.filter(lambda r: r["n_timesteps"] >= 2048)
events = ds.filter(lambda r: r["has_event"])
```

Or select shards up front from [`manifest.json`](manifest.json) and hand the URLs to your loader —
that is the cheap path, because it skips the shards entirely rather than filtering their rows.

One caveat worth knowing before you build a training loop: a sequential stream reads one shard at
a time, but `ds.shuffle(buffer_size=N)` in streaming mode has to pull from many shards at once. On
this corpus that is expensive — records here are whole multi-thousand-step series, so a measured
`buffer_size=64` shuffle moved **3.0 GB** over the wire to yield 6 records, against 93 MB for a
10-record sequential read. Shuffle at the *shard* level from the manifest, and reserve the
in-stream buffer for small values.

---

## 1. Format, and why Parquet rather than WebDataset

Parquet shards under `data/<source>/{train,val}-NNNNN.parquet`, zstd-compressed, mean
**146 MB** per shard (min 0.0 MB, max 352 MB).

The plan allowed WebDataset `.tar` **or** Parquet. Parquet was chosen because:

1. **Zero-dependency streaming.** `datasets.load_dataset(..., streaming=True)` reads Parquet
   natively over HTTP range requests with no loader script; `webdataset` would be a new
   dependency for every consumer of the corpus.
2. **Column projection.** A dataloader that needs only `values` + `entity_id` never transfers the
   ~15 provenance/label columns. A `.tar` member is opaque — you pay for all of it or none.
3. **The tags stay queryable server-side.** The HF dataset viewer and its DuckDB endpoint index
   Parquet, so `source` / `domain` / `quality_tags` / `has_event` can be counted and filtered
   without downloading a byte. That is what makes §4's "splits are queries, not copies" real.

Shards are **single-source and single-split** so that every manifest row describes a homogeneous
unit, and the shard target is set from a per-source compression ratio that the build measures and
feeds back (zstd runs 0.12x on USHCN's integer-valued daily climate and 0.83x on ERA5 reanalysis
floats — a 7x spread, so a single fixed record count would miss the band badly).

Stated plainly: **most shards here are below the 100 MB floor.** Two reasons, both structural.
Many cleared sources hold less than 100 MB in total, so they cannot fill one shard. And the first
shard of each source is sized against a deliberately pessimistic 0.7 ratio (so it can never blow
past 500 MB), which undershoots for the highly compressible sources. The band is met where it
matters: `weatherbench_daily`, which is **81%** of the corpus by bytes, shards at
301 MB (min 88, max 352).

### Per-record schema

| field | type | meaning |
|---|---|---|
| `series_id` | string | `"<source>:<native id>"`, globally unique |
| `source` | string | audited config name (row in the provenance table) |
| `domain` | string | energy / climate / traffic / web / health / econ / retail / other |
| `native_C` | int32 | number of channels **as shipped by the source** — no padding, no grouping |
| `channel_names` | list[string] | the upstream column name of each channel, in order |
| `values` | list[list[float32]] | **raw `(C, T)`** — un-patched, un-windowed, un-normalized |
| `n_timesteps` | int32 | `T` |
| `freq` | string | inferred sampling frequency (`15min`, `h`, `D`, `W`, `M`, `Q`, `Y`, …) |
| `start_time_ms` | int64 | epoch-ms of `t=0`. Series are regularly sampled at `freq`; records where they are not carry the `irregular_timestamps` tag |
| `entity_id` | string | physical entity (station, grid cell, household, page) — **the split key** |
| `quality_tags` | list[string] | see §5 |
| `split_role` | string | `train` \| `val` (entity-disjoint, §4) |
| `has_classification`, `class_label` | bool, int32 | **always false / −1** — no source here ships native class labels |
| `has_ad` | bool | **always false** — no source here ships native anomaly labels |
| `has_event`, `event_label_origin` | bool, string | `added_by_us` under one deterministic rule, §3 |
| `event_rule_id`, `event_description` | string | which rule, and the physical event it encodes |
| `event_threshold`, `event_horizon`, `event_calib_end`, `event_channel` | float32, int32×3 | the parameters that **exactly** reconstruct the labels |
| `n_event_positives` | int32 | positives under that rule, so you can filter by positive rate for free |
| `license`, `license_url` | string | the redistribution basis for this record |

**Timestamps are not stored per step.** `start_time_ms` + `freq` reconstructs the index exactly for
regular series and halves the on-disk size (an `int64` per step costs more than the `float32` value
it labels). Records whose upstream index is not regular are tagged `irregular_timestamps`.

---

## 2. What is here, and what is deliberately not

The audit covered the **51**-config real-world Chronos-2 subset
(`autogluon/chronos_datasets` minus the synthetic `training_corpus_*` and the 14
`weatherbench_hourly_*` configs). **The published corpus and the corpus we pretrain on are
different sets**, because *hosting on the Hub is redistribution and downloading under a
source's own research-use terms is not*. Both gates are applied separately:

| set | n | where it lives |
|---|---|---|
| audited real-world Chronos-2 subset | 51 | — |
| − non-redistributable (6 `EXCLUDE` + 4 `LINK_ONLY`) | −10 | local pretraining only; **never** on the Hub |
| − conditional, pending Zenodo re-sourcing (`m4_*`×6, `nn5`, `solar`) | −9 | local only; **deferred** from the Hub |
| = license-cleared for redistribution | **32** | — |
| − contributed zero usable records (every series shorter than 64 steps) | −4 | dropped at build time |
| − cleared but not yet built | −0 | see the resume command in §8 |
| **= published `Forgis/hepa-corpus` v1** | **28** | **this repo** |

The **32** cleared configs are selected by the predicate
`verdict == "INCLUDE" and redistributable is True and config not in DEFERRED_CONDITIONAL`, and
**28** of them actually contributed data. The ones that did not:

| source | domain | why it contributed nothing |
|---|---|---|
| `monash_car_parts` | retail | every series is shorter than 64 steps |
| `monash_m1_yearly` | other | every series is shorter than 64 steps |
| `monash_m3_yearly` | other | every series is shorter than 64 steps |
| `monash_tourism_yearly` | econ | every series is shorter than 64 steps |

These are low-frequency economic and demographic series — a 20-point yearly index cannot form even
two `P=16` patches, and this repo's existing corpus loader already uses a stricter `min_len=256`.
They are dropped, not silently counted.

The gate keys on the `redistributable` **boolean**, not on a text match for `"EXCLUDE"` — a text
match would silently admit the four `LINK_ONLY` configs, which are equally non-redistributable.

If you pretrain on this repo and compare against a HEPA number computed on the 51-config local
corpus, **the corpora differ** and some of any gap is data, not method. In particular
`monash_rideshare` — one of only three natively-multivariate configs in the report's set — is
excluded here, so the published corpus is *less* natively-MV than the one the report used.

### Not redistributed (never uploaded, in any form)

| source | verdict | domain | origin | why it is absent |
|---|---|---|---|---|
| `dominick` | EXCLUDE | retail | [origin](https://www.chicagobooth.edu/research/kilts/research-data/dominicks) | Kilts Center states 'These data are for academic research purposes only' with no redistribution grant; Monash's CC-BY-4.0 Zenodo re-deposit (10.5281/zenodo.4654802) cannot cure the upstream owner's restriction, so we do not re-host. |
| `exchange_rate` | EXCLUDE | econ | [origin](https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate) | The autogluon card claims MIT but the source repository has NO LICENSE file (GitHub license API returns 404) and its README names no upstream FX data provider; UNVERIFIED -> EXCLUDE. |
| `m5` | EXCLUDE | retail | [origin](https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules) | Proprietary Walmart retail data released only under Kaggle competition rules; the rules page is JavaScript-gated and could not be retrieved, so no redistribution grant can be cited. UNVERIFIED -> EXCLUDE. Kaggle's standard competition-data clause is non-commercial and participant-scoped in any case. |
| `mexico_city_bikes` | EXCLUDE | traffic | [origin](https://ecobici.cdmx.gob.mx/en/open-data/) | The Ecobici open-data page and its terms-and-conditions page state no license, no reuse grant and no named open-data licence; the CDMX open-data portal terms page was unreachable. UNVERIFIED -> EXCLUDE. |
| `monash_fred_md` | EXCLUDE | econ | [origin](https://zenodo.org/records/4654833) | FRED's legal terms forbid redistributing third-party proprietary content without the copyright holder's written permission and require permission for 'pre-approval required' copyrighted series, and the St. Louis Fed states it cannot grant permission on their behalf; FRED-MD aggregates such third-party series. Monash's CC-BY-4.0 re-deposit cannot cure this, so we do not re-host. Lead judgment requested. |
| `monash_rideshare` | EXCLUDE | traffic | [origin](https://zenodo.org/records/5122232) | Weakest chain of title in the corpus: the underlying data is an individual's scrape of Uber and Lyft commercial pricing APIs posted to Kaggle with no verifiable license, so neither Kaggle nor Monash can be shown to have had the right to license it CC-BY-4.0. Lead judgment requested. |
| `taxi_1h` | LINK_ONLY | traffic | [origin](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) | The AWS Registry of Open Data lists the TLC trip records' license as the NYC.gov Terms of Use, which state 'All rights are reserved' and grant no reuse; the Apache-2.0 license on the gluon-ts fork covers that repository, not the City's underlying data. Reference the TLC source, do not re-host. |
| `taxi_30min` | LINK_ONLY | traffic | [origin](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) | The AWS Registry of Open Data lists the TLC trip records' license as the NYC.gov Terms of Use, which state 'All rights are reserved' and grant no reuse; the Apache-2.0 license on the gluon-ts fork covers that repository, not the City's underlying data. Reference the TLC source, do not re-host. |
| `uber_tlc_daily` | LINK_ONLY | traffic | [origin](https://github.com/fivethirtyeight/uber-tlc-foil-response) | The FiveThirtyEight repository has NO LICENSE file (GitHub license API returns 404) and the data is a FOIL disclosure of NYC TLC records whose publisher reserves all rights. UNVERIFIED -> reference link-only, never re-host. |
| `uber_tlc_hourly` | LINK_ONLY | traffic | [origin](https://github.com/fivethirtyeight/uber-tlc-foil-response) | The FiveThirtyEight repository has NO LICENSE file (GitHub license API returns 404) and the data is a FOIL disclosure of NYC TLC records whose publisher reserves all rights. UNVERIFIED -> reference link-only, never re-host. |

`LINK_ONLY` sources are reachable at their origin link above; we do not re-host them. The four
NYC TLC configs are `LINK_ONLY` because NYC.gov's Terms of Use read "All rights are reserved" —
the Apache-2.0 on the gluon-ts fork covers that repository, not the City's data.

### Deferred — cleared in principle, not shipped yet

| source | license basis | status |
|---|---|---|
| `m4_daily` | [CC-BY-4.0](https://zenodo.org/records/4656548) | `requires_resourcing_from_zenodo` |
| `m4_hourly` | [CC-BY-4.0](https://zenodo.org/records/4656589) | `requires_resourcing_from_zenodo` |
| `m4_monthly` | [CC-BY-4.0](https://zenodo.org/records/4656480) | `requires_resourcing_from_zenodo` |
| `m4_quarterly` | [CC-BY-4.0](https://zenodo.org/records/4656410) | `requires_resourcing_from_zenodo` |
| `m4_weekly` | [CC-BY-4.0](https://zenodo.org/records/4656522) | `requires_resourcing_from_zenodo` |
| `m4_yearly` | [CC-BY-4.0](https://zenodo.org/records/4656379) | `requires_resourcing_from_zenodo` |
| `nn5` | [CC-BY-4.0](https://zenodo.org/records/4656117) | `requires_resourcing_from_zenodo` |
| `solar` | [CC-BY-4.0](https://zenodo.org/records/4656144) | `requires_resourcing_from_zenodo` |
| `solar_1h` | [CC-BY-4.0](https://zenodo.org/records/4656144) | `requires_resourcing_from_zenodo` |

**Action required before these can ship.** Re-source the identical series from the Monash Time
Series Forecasting Repository Zenodo deposit named in the license link and re-attribute to
Godahewa et al. (2021). They are *not* shipped from the `autogluon` copy because the origin that
copy points at carries no license at all — `Mcompetitions/M4-methods` has no `LICENSE` file, NN5
publishes none, and NREL's own disclaimer was unreachable. Shipping those bytes while citing
Monash's CC-BY-4.0 would mean **the citation does not describe the artifact**, which is precisely
the defect the audit exists to prevent.

---

## 3. Labels

### `has_classification` / `has_AD`: native only — and there are none

Every source here is a forecasting corpus. None ships class labels or anomaly annotations, so
both flags are `false` on **every** record. We did not synthesize either: there is no rule over
these series whose ground truth is certain, and a plausible-looking guess in a public corpus is
worse than an absent column.

### `has_event`: one deterministic rule, applied only where the event is physical

`event_label_origin` is `added_by_us` wherever it is set — no source ships native event labels.
The single rule used is:

```
p95_threshold_crossing_v1:
y[t] = 1 iff max(values[event_channel, t:t+H]) > thr, where thr = P95(values[event_channel, :calib_end]) computed on the DISJOINT calibration region values[:, :calib_end], calib_end = floor(0.30*T), and labels are defined only for calib_end <= t <= T-H. Deterministic: no fitting, no heuristics, exactly reproducible from (event_threshold, event_horizon, event_calib_end, event_channel).
```

Reconstruct labels exactly:

```python
import numpy as np
x   = np.asarray(r["values"], np.float32)[r["event_channel"]]
H, c = r["event_horizon"], r["event_calib_end"]
seg = x[c:]
w   = np.lib.stride_tricks.sliding_window_view(seg, H)[: len(seg) - H]
y   = np.nanmax(w, axis=1) > r["event_threshold"]     # labels for t = c .. T-H-1
assert y.sum() == r["n_event_positives"]
```

This is deterministic — no fitting, no tuning, no heuristic — and the calibration region
`values[:, :calib_end]` is **disjoint** from every labelled timestep, so the threshold cannot leak
the label.

It is applied **only** where a P95 crossing is a recognised physical event, and only to records
with `T ≥ 512` (below that a 30% calibration region is too small for a stable P95):

| source | records labelled | event |
|---|---|---|
| `electricity_15min` | 370 / 370 | load spike (P95 15-min household demand exceedance) |
| `ercot` | 8 / 8 | grid load spike (P95 zonal demand exceedance) |
| `monash_australian_electricity` | 5 / 5 | grid load spike (P95 half-hourly demand exceedance) |
| `monash_electricity_hourly` | 321 / 321 | load spike (P95 hourly client demand exceedance) |
| `monash_kdd_cup_2018` | 268 / 270 | air-quality episode (P95 pollutant concentration exceedance) |
| `monash_london_smart_meters` | 5,555 / 5,559 | load spike (P95 half-hourly household demand exceedance) |
| `monash_pedestrian_counts` | 66 / 66 | crowd surge (P95 hourly pedestrian count exceedance) |
| `monash_saugeenday` | 1 / 1 | high-flow / flood peak (P95 daily river flow exceedance) |
| `monash_temperature_rain` | 422 / 422 | heat extreme (P95 daily mean-temperature exceedance) |
| `monash_traffic` | 862 / 862 | congestion event (P95 hourly occupancy exceedance) |
| `monash_weather` | 3,010 / 3,010 | per-variable, see below |
| `ushcn_daily` | 1,216 / 1,218 | heavy-precipitation event (P95 daily PRCP exceedance) |
| `weatherbench_daily` | 34,816 / 225,280 | per-variable, see below |
| `weatherbench_weekly` | 34,816 / 225,280 | per-variable, see below |
| `wiki_daily_100k` | 100,000 / 100,000 | traffic surge (P95 daily pageview exceedance) |
| `wind_farms_hourly` | 328 / 328 | high-output wind event (P95 hourly farm power exceedance) |

`weatherbench_*` and `monash_weather` each bundle several distinct physical fields under one
config name, with the field in a `subset` column (exported as the `variable:<name>` quality tag).
For those, eligibility and wording are resolved per **variable**:

| source | variable | event |
|---|---|---|
| `monash_weather` | `maxtemp` | heat extreme (P95 exceedance of daily maximum temperature) |
| `monash_weather` | `mintemp` | warm-night extreme (P95 exceedance of daily minimum temperature) |
| `monash_weather` | `rain` | heavy-precipitation event (P95 exceedance of daily rainfall) |
| `monash_weather` | `solar` | high-insolation day (P95 exceedance of daily solar exposure) |
| `weatherbench_daily` | `10m_u_component_of_wind` | high-wind event (P95 exceedance of 10m u-wind) |
| `weatherbench_daily` | `10m_v_component_of_wind` | high-wind event (P95 exceedance of 10m v-wind) |
| `weatherbench_daily` | `10m_wind_speed` | high-wind event (P95 exceedance of 10m wind speed) |
| `weatherbench_daily` | `2m_temperature` | heat extreme (P95 exceedance of 2m temperature) |
| `weatherbench_daily` | `temperature` | warm anomaly (P95 exceedance of air temperature) |
| `weatherbench_daily` | `total_precipitation` | heavy-precipitation event (P95 exceedance of total precipitation) |
| `weatherbench_weekly` | `10m_u_component_of_wind` | high-wind event (P95 exceedance of 10m u-wind) |
| `weatherbench_weekly` | `10m_v_component_of_wind` | high-wind event (P95 exceedance of 10m v-wind) |
| `weatherbench_weekly` | `10m_wind_speed` | high-wind event (P95 exceedance of 10m wind speed) |
| `weatherbench_weekly` | `2m_temperature` | heat extreme (P95 exceedance of 2m temperature) |
| `weatherbench_weekly` | `temperature` | warm anomaly (P95 exceedance of air temperature) |
| `weatherbench_weekly` | `total_precipitation` | heavy-precipitation event (P95 exceedance of total precipitation) |

**Everything else gets `has_event=false`.** That deliberately includes the low-frequency economic
and demographic series (`monash_m1_*`, `monash_m3_*`, `monash_tourism_*`, `monash_cif_2016`,
`monash_car_parts`, `monash_hospital`, `monash_nn5_weekly`) — on a yearly or quarterly index a P95
crossing is trend, not an event — and the weatherbench fields not listed above (`geopotential`,
`potential_vorticity`, pressure-level winds, `total_cloud_cover`,
`toa_incident_solar_radiation`), where we could not assert certain ground truth.

---

## 4. Methodology

### Normalization — in the dataloader, never on disk

Nothing here is normalized, and that is deliberate. Level and scale carry signal (exp-08 found
that RevIN made a HEPA encoder **blind to per-window level** on energy events), so the choice of
normalization is a modelling decision that must stay ablatable. Crop the raw `(C, T)` first, then
normalize the crop in the worker. Same argument for **patch size and window length**: they are
never baked in, so `P=16` and `T=2048` are choices a consumer makes, not constraints this corpus
imposes.

### Pseudo-multivariate grouping — also downstream

Records are stored at their **native** `C`. Most sources here are univariate (`native_C=1`); only
`ushcn_daily` (5 channels) and `monash_temperature_rain` (78) are natively multivariate. The
HEPA recipe groups `c_group=8` univariate series from the *same* source into a `(T, C=8)` sample,
sampling with replacement where fewer than 8 are usable, with each source contributing equally per
epoch (balanced sampling). That grouping is **not** materialised on disk: it is a sampler choice,
it changes between corpus iterations, and freezing it would make `native_C` a lie. Group at load
time, over records filtered from one `source`.

### De-duplication

Within a source, series are unique by upstream id. Across sources, overlap is structural rather
than incidental (e.g. `monash_electricity_hourly` and `electricity_15min` are different
resamplings of the same UCI donation), so it is handled by **provenance, not by hashing**: the
`source` field and the manifest let a consumer drop a whole lineage. Records that are constant, or
have no finite values, or are shorter than 64 steps, are dropped at build time. Records that
merely contain some NaNs are **kept and tagged** `has_nan` — missingness is signal, and masking it
is the dataloader's decision, not ours.

### Splits are entity-disjoint, and are queries over the manifest

`split_role` is assigned by `val iff sha1(entity_id)[:8] % 100 < 5` — a hash of the **entity**, never a
random row or window split. All records of one physical entity (a USHCN station, a WeatherBench
grid cell, a London household, a Wikipedia page) land on the same side, so a train/val pair never
shares a device. `entity_id` is exported on every record so you can re-split by entity or by
contiguous time blocks with a buffer gap without re-downloading anything.

The 5% is global, not per-source, so a source with few entities can land **entirely** in `train`
and have no `val-*` shard at all (`monash_kdd_cup_2018`, for instance, has 59 entities and drew
zero). That is a property of an honest entity-level split at small `n`, not a bug — but if you
need per-source validation, re-split on `entity_id` yourself rather than assuming every source is
represented in `validation`.

Because a shard is single-source and single-split, **iterations and splits are manifest queries**:

```python
import json, urllib.request
m = json.load(urllib.request.urlopen(
    "https://huggingface.co/datasets/Forgis/hepa-corpus/resolve/main/manifest.json"))
v1_train = [s["shard"] for s in m["shards"]
            if s["split_role"] == "train" and "v1-chronos2" in s["corpus_versions"]]
energy_events = [s["shard"] for s in m["shards"]
                 if s["domain"] == "energy" and s["label_flags"]["has_event"]]
```

Adding corpus v2/v3 means appending shards with new `corpus_versions` tags — never re-sharding,
never copying.

### OOD test set: held out entirely

The six downstream event-forecasting evaluation datasets —
**C-MAPSS FD001, C-MAPSS FD002, C-MAPSS FD003, ETTm1, GECCO, PSM** — contribute **zero series** to this corpus. None of
them is a `chronos_datasets` config, and the build asserts every uploaded source name against the
held-out set. Frozen-encoder transfer to those six is therefore genuinely zero-shot with respect
to this corpus.

---

## 5. Composition

| domain | sources | shards | records | rec % | channel-timesteps | ct % | size |
|---|---:|---:|---:|---:|---:|---:|---:|
| climate | 7 | 53 | 455,481 | 80.2% | 4,066.1 M | 88.8% | 13.13 GB |
| web | 1 | 3 | 100,000 | 17.6% | 274.1 M | 6.0% | 0.33 GB |
| energy | 8 | 15 | 7,240 | 1.3% | 222.3 M | 4.9% | 0.35 GB |
| traffic | 2 | 4 | 928 | 0.2% | 18.3 M | 0.4% | 0.04 GB |
| other | 6 | 12 | 3,037 | 0.5% | 0.3 M | 0.0% | 0.00 GB |
| econ | 4 | 8 | 951 | 0.2% | 0.2 M | 0.0% | 0.00 GB |
| **total** | **28** | **95** | **567,637** | 100% | **4,581.3 M** | 100% | **13.84 GB** |

`quality_tags` vocabulary: `corpus_v1_chronos2`, `domain:<d>`, `freq:<f>`, `variable:<v>`,
`univariate` / `native_mv`, `has_nan`, `irregular_timestamps`,
`len_lt_512` / `len_ge_512` / `len_ge_2048`, `event_labeled`, `weak_chain_of_title`,
`copernicus_notice_required`.

### Scale, stated against the target

The exp-09 plan targeted a **Base-scale ~250–500 GB** corpus. The realised v1 is **13.8 GB**.
That gap is not a shortfall of the pipeline — it is the size of the license-clean real-world
Chronos-2 subset itself. The entire `autogluon/chronos_datasets` repo is ~895 GB, but 779 GB of
that is `weatherbench_hourly_*` and 91 GB is synthetic `training_corpus_*`, both of which the
report's own corpus definition excludes; the remaining real-world subset is ~25 GB before the
licensing gate removes a further 19 configs. **Reaching 250–500 GB requires more sources
(LOTSA, v2), not more of these** — the pipeline streams and scales, the data does not exist at
that size under this corpus definition.

---

## 6. Provenance

One row per audited source. `used_in_pretraining` and `redistributed` are **independent**:
everything is used locally under its own research-use terms; only the cleared subset is
redistributed here.

| source | domain | origin link | license | #series | native C | length range | freq | has_classification | has_AD | has_event | event_label_origin | used_in_pretraining | redistributed |
|---|---|---|---|---:|---|---|---|:--:|:--:|:--:|---|:--:|---|
| `dominick` | retail | [origin](https://www.chicagobooth.edu/research/kilts/research-data/dominicks) | [proprietary-academic-use-only](https://www.chicagobooth.edu/research/kilts/research-data/dominicks) | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `electricity_15min` | energy | [origin](https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014) | [CC-BY-4.0](https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014) | 370 | 1 | 16,032–140,256 | 15min | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `ercot` | energy | [origin](https://www.ercot.com/gridinfo/load) | [ERCOT-Terms-of-Use (redistribution expressly permitted)](https://www.ercot.com/help/terms) | 8 | 1 | 154,872–154,872 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `exchange_rate` | econ | [origin](https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate) | [unknown]() | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `m4_daily` | other | [origin](https://zenodo.org/records/4656548) | [CC-BY-4.0](https://zenodo.org/records/4656548) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m4_hourly` | other | [origin](https://zenodo.org/records/4656589) | [CC-BY-4.0](https://zenodo.org/records/4656589) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m4_monthly` | other | [origin](https://zenodo.org/records/4656480) | [CC-BY-4.0](https://zenodo.org/records/4656480) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m4_quarterly` | other | [origin](https://zenodo.org/records/4656410) | [CC-BY-4.0](https://zenodo.org/records/4656410) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m4_weekly` | other | [origin](https://zenodo.org/records/4656522) | [CC-BY-4.0](https://zenodo.org/records/4656522) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m4_yearly` | other | [origin](https://zenodo.org/records/4656379) | [CC-BY-4.0](https://zenodo.org/records/4656379) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `m5` | retail | [origin](https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules) | [unknown (Kaggle competition rules, not retrievable)]() | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `mexico_city_bikes` | traffic | [origin](https://ecobici.cdmx.gob.mx/en/open-data/) | [unknown]() | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `monash_australian_electricity` | energy | [origin](https://zenodo.org/records/4659727) | [CC-BY-4.0](https://zenodo.org/records/4659727) | 5 | 1 | 230,736–232,272 | 30min | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_car_parts` | retail | [origin](https://zenodo.org/records/4656022) | [CC-BY-4.0](https://zenodo.org/records/4656022) | — | — | — | — | no | no | — | — | yes | no — *cleared, but not yet built (see §5)* |
| `monash_cif_2016` | econ | [origin](https://zenodo.org/records/4656042) | [CC-BY-4.0](https://zenodo.org/records/4656042) | 60 | 1 | 65–120 | M | no | no | no | n/a | yes | **yes** |
| `monash_covid_deaths` | other | [origin](https://zenodo.org/records/4656009) | [CC-BY-4.0](https://zenodo.org/records/4656009) | 233 | 1 | 212–212 | D | no | no | no | n/a | yes | **yes** |
| `monash_electricity_hourly` | energy | [origin](https://zenodo.org/records/4656140) | [CC-BY-4.0](https://zenodo.org/records/4656140) | 321 | 1 | 26,304–26,304 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_electricity_weekly` | energy | [origin](https://zenodo.org/records/4656141) | [CC-BY-4.0](https://zenodo.org/records/4656141) | 321 | 1 | 156–156 | W | no | no | no | n/a | yes | **yes** |
| `monash_fred_md` | econ | [origin](https://zenodo.org/records/4654833) | [conflicting: Zenodo says CC-BY-4.0, but FRED legal terms restrict third-party series](https://fred.stlouisfed.org/legal/) | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `monash_hospital` | other | [origin](https://zenodo.org/records/4656014) | [CC-BY-4.0](https://zenodo.org/records/4656014) | 767 | 1 | 84–84 | M | no | no | no | n/a | yes | **yes** |
| `monash_kdd_cup_2018` | climate | [origin](https://zenodo.org/records/4656756) | [CC-BY-4.0](https://zenodo.org/records/4656756) | 270 | 1 | 9,504–10,920 | h | no | no | partial (268/270) | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_london_smart_meters` | energy | [origin](https://zenodo.org/records/4656091) | [CC-BY-4.0](https://zenodo.org/records/4656091) | 5,559 | 1 | 288–39,648 | 30min | no | no | partial (5555/5559) | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_m1_monthly` | other | [origin](https://zenodo.org/records/4656159) | [CC-BY-4.0](https://zenodo.org/records/4656159) | 479 | 1 | 65–150 | M | no | no | no | n/a | yes | **yes** |
| `monash_m1_quarterly` | other | [origin](https://zenodo.org/records/4656154) | [CC-BY-4.0](https://zenodo.org/records/4656154) | 44 | 1 | 64–114 | Q | no | no | no | n/a | yes | **yes** |
| `monash_m1_yearly` | other | [origin](https://zenodo.org/records/4656193) | [CC-BY-4.0](https://zenodo.org/records/4656193) | — | — | — | — | no | no | — | — | yes | no — *cleared, but not yet built (see §5)* |
| `monash_m3_monthly` | other | [origin](https://zenodo.org/records/4656298) | [CC-BY-4.0](https://zenodo.org/records/4656298) | 1,428 | 1 | 66–144 | M | no | no | no | n/a | yes | **yes** |
| `monash_m3_quarterly` | other | [origin](https://zenodo.org/records/4656262) | [CC-BY-4.0](https://zenodo.org/records/4656262) | 86 | 1 | 64–72 | Q | no | no | no | n/a | yes | **yes** |
| `monash_m3_yearly` | other | [origin](https://zenodo.org/records/4656222) | [CC-BY-4.0](https://zenodo.org/records/4656222) | — | — | — | — | no | no | — | — | yes | no — *cleared, but not yet built (see §5)* |
| `monash_nn5_weekly` | econ | [origin](https://zenodo.org/records/4656125) | [CC-BY-4.0](https://zenodo.org/records/4656125) | 111 | 1 | 113–113 | W | no | no | no | n/a | yes | **yes** |
| `monash_pedestrian_counts` | traffic | [origin](https://zenodo.org/records/4656626) | [CC-BY-4.0](https://zenodo.org/records/4656626) | 66 | 1 | 576–96,424 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_rideshare` | traffic | [origin](https://zenodo.org/records/5122232) | [conflicting: Zenodo says CC-BY-4.0, upstream provenance is scraped commercial API data](https://zenodo.org/records/5122232) | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `monash_saugeenday` | climate | [origin](https://zenodo.org/records/4656058) | [CC-BY-4.0](https://zenodo.org/records/4656058) | 1 | 1 | 23,741–23,741 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_temperature_rain` | climate | [origin](https://zenodo.org/records/5129091) | [CC-BY-4.0](https://zenodo.org/records/5129091) | 422 | 76 | 725–725 | D | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_tourism_monthly` | econ | [origin](https://zenodo.org/records/4656096) | [CC-BY-4.0](https://zenodo.org/records/4656096) | 366 | 1 | 91–333 | M | no | no | no | n/a | yes | **yes** |
| `monash_tourism_quarterly` | econ | [origin](https://zenodo.org/records/4656093) | [CC-BY-4.0](https://zenodo.org/records/4656093) | 414 | 1 | 64–130 | Q | no | no | no | n/a | yes | **yes** |
| `monash_tourism_yearly` | econ | [origin](https://zenodo.org/records/4656103) | [CC-BY-4.0](https://zenodo.org/records/4656103) | — | — | — | — | no | no | — | — | yes | no — *cleared, but not yet built (see §5)* |
| `monash_traffic` | traffic | [origin](https://zenodo.org/records/4656132) | [CC-BY-4.0](https://zenodo.org/records/4656132) | 862 | 1 | 17,544–17,544 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `monash_weather` | climate | [origin](https://zenodo.org/records/4654822) | [CC-BY-4.0](https://zenodo.org/records/4654822) | 3,010 | 1 | 1,332–65,981 | D | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `nn5` | econ | [origin](https://zenodo.org/records/4656117) | [CC-BY-4.0](https://zenodo.org/records/4656117) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `solar` | energy | [origin](https://zenodo.org/records/4656144) | [CC-BY-4.0](https://zenodo.org/records/4656144) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `solar_1h` | energy | [origin](https://zenodo.org/records/4656144) | [CC-BY-4.0](https://zenodo.org/records/4656144) | — | — | — | — | no | no | — | — | yes | no — *deferred pending Zenodo re-sourcing* |
| `taxi_1h` | traffic | [origin](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) | [NYC.gov Terms of Use - all rights reserved (no redistribution grant)](https://www.nyc.gov/home/terms-of-use.page) | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `taxi_30min` | traffic | [origin](https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page) | [NYC.gov Terms of Use - all rights reserved (no redistribution grant)](https://www.nyc.gov/home/terms-of-use.page) | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `uber_tlc_daily` | traffic | [origin](https://github.com/fivethirtyeight/uber-tlc-foil-response) | [unknown]() | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `uber_tlc_hourly` | traffic | [origin](https://github.com/fivethirtyeight/uber-tlc-foil-response) | [unknown]() | — | — | — | — | no | no | — | — | yes | no — *not redistributed here* |
| `ushcn_daily` | climate | [origin](https://www.ncei.noaa.gov/products/land-based-station/us-historical-climatology-network) | [US-Government-Public-Domain (17 U.S.C. Sec. 105)](https://library.noaa.gov/blogs/news-research-highlights/question-of-the-quarter-noaacopyright) | 1,218 | 5 | 5,906–59,283 | D | no | no | partial (1216/1218) | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `weatherbench_daily` | climate | [origin](https://mediatum.ub.tum.de/1524895) | [CC-BY-4.0](https://mediatum.ub.tum.de/1524895) | 225,280 | 1 | 14,609–14,610 | D | no | no | partial (34816/225280) | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `weatherbench_weekly` | climate | [origin](https://mediatum.ub.tum.de/1524895) | [CC-BY-4.0](https://mediatum.ub.tum.de/1524895) | 225,280 | 1 | 2,087–2,087 | W | no | no | partial (34816/225280) | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `wiki_daily_100k` | web | [origin](https://dumps.wikimedia.org/other/pageviews/readme.html) | [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/) | 100,000 | 1 | 2,741–2,741 | D | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |
| `wind_farms_daily` | energy | [origin](https://zenodo.org/records/4654858) | [CC-BY-4.0](https://zenodo.org/records/4654858) | 328 | 1 | 71–366 | D | no | no | no | n/a | yes | **yes** |
| `wind_farms_hourly` | energy | [origin](https://zenodo.org/records/4654858) | [CC-BY-4.0](https://zenodo.org/records/4654858) | 328 | 1 | 1,715–8,784 | h | no | no | yes | added_by_us (`p95_threshold_crossing_v1`) | yes | **yes** |

---

## 7. Licensing

Hosting on the Hub is **redistribution**. A source ships here only if its license permits
redistribution *and* research use, verified against the upstream rights holder — not merely
against whatever a downstream re-publisher asserted. Redistribution basis for every uploaded
source:

| license | sources | which |
|---|---:|---|
| [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | 25 | `electricity_15min`, `monash_australian_electricity`, `monash_cif_2016`, `monash_covid_deaths`, `monash_electricity_hourly`, `monash_electricity_weekly`, `monash_hospital`, `monash_kdd_cup_2018`, `monash_london_smart_meters`, `monash_m1_monthly`, `monash_m1_quarterly`, `monash_m3_monthly`, `monash_m3_quarterly`, `monash_nn5_weekly`, `monash_pedestrian_counts`, `monash_saugeenday`, `monash_temperature_rain`, `monash_tourism_monthly`, `monash_tourism_quarterly`, `monash_traffic`, `monash_weather`, `weatherbench_daily`, `weatherbench_weekly`, `wind_farms_daily`, `wind_farms_hourly` |
| ERCOT-Terms-of-Use (redistribution expressly permitted) | 1 | `ercot` |
| US-Government-Public-Domain (17 U.S.C. Sec. 105) | 1 | `ushcn_daily` |
| [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/) | 1 | `wiki_daily_100k` |

### Caveat — Monash chain of title

12 uploaded sources rest on the Monash Time Series Forecasting Repository's CC-BY-4.0
Zenodo assertion over data Monash **did not originate**: `monash_australian_electricity`, `monash_cif_2016`, `monash_kdd_cup_2018`, `monash_m1_monthly`, `monash_m1_quarterly`, `monash_m3_monthly`, `monash_m3_quarterly`, `monash_tourism_monthly`, `monash_tourism_quarterly`, `monash_traffic`, `wind_farms_daily`, `wind_farms_hourly`.

We ship them because Monash is a citable, DataCite-registered scholarly deposit and the upstream
material is public — but the chain of title is *weaker* than a direct grant from the originator,
and you should know that before building on them. Four Monash configs have genuinely clean chains
and carry no such flag: `monash_covid_deaths` (JHU CSSE is itself CC-BY-4.0),
`monash_electricity_hourly` / `monash_electricity_weekly` (UCI CC-BY-4.0),
`monash_london_smart_meters` (London Datastore CC-BY). Records from flagged sources carry the
`weak_chain_of_title` quality tag, so you can exclude them with one filter.

### Required notice — Copernicus (`weatherbench_daily`, `weatherbench_weekly`)

> Contains modified Copernicus Climate Change Service Information [2019]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus Information or Data it contains.

Records from these sources carry the `copernicus_notice_required` tag. If you redistribute them
onward, carry this notice.

### Attribution

Attribute per source using the provenance table. `ushcn_daily` is US federal public domain
(17 U.S.C. §105) but still asks for the Menne et al. (2009) citation. Monash-sourced configs
should cite Godahewa et al. (2021). The upstream aggregation is
[`autogluon/chronos_datasets`](https://huggingface.co/datasets/autogluon/chronos_datasets).

### Report a problem

If you are a rights holder and believe something here should not be, open a discussion on this
repo and we will remove it immediately, before adjudicating.

---

## 8. Reproducing this build

```bash
export HEPA_DATA_DIR=$HOME/.hepa/data
python3 experiments/exp-09/scripts/build_hf_corpus.py --configs all-cleared --shard-mb 300
```

The build is resumable — it checkpoints per shard and re-running skips completed sources. It will
**refuse** any config that is not license-cleared, by name, at the command line.

Built 2026-08-05T12:08:51.769655+00:00 · manifest version 1.0.