Datasets:
File size: 3,222 Bytes
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license: cc-by-nc-sa-4.0
task_categories:
- time-series-forecasting
language:
- en
tags:
- finance
- multivariate
- time-series
- panel-data
configs:
- config_name: close
data_files:
- split: train
path: data/close/train-*.parquet
- split: test
path: data/close/test-*.parquet
- config_name: volume
data_files:
- split: train
path: data/volume/train-*.parquet
- split: test
path: data/volume/test-*.parquet
---
# Finance1K
[](https://arxiv.org/abs/2608.14106)
Finance1K is a synchronized hourly panel for next-step forecasting across
1,000 US equities. It contains 28,510 chronological observations from 2015 to
early 2026 and provides two aligned targets:
- close: hourly close log return, log(close_t / close_t-1);
- volume: hourly log-volume change, log(volume_t / volume_t-1).
The two configurations use the same timestamps, ticker order, validity mask,
and chronological split. The first 19,957 rows form the training set and the
following 8,553 rows form the test set.
## Data structure
Each Parquet row is one timestamp and one synchronized cross-section. The
values and valid fields are fixed-size arrays of length 1,000. Array position j
matches selection_order j in metadata/tickers.parquet.
| Field | Type | Meaning |
|---|---|---|
| row_index | int64 | Global chronological row index |
| timestamp | int64 | UTC Unix timestamp in seconds |
| values | float32[1000] | Close or volume target cross-section |
| valid | bool[1000] | Entry-level validity mask before zero fill |
| xs_vol | float32 | Cross-sectional population standard deviation |
| regime | int8 | Calm/stress indicator fitted on training rows |
| lagged_vol | float32 | Causal 24-row trailing volatility proxy |
| ceiling_series | float32 | Training-normalized predictability proxy |
The metadata directory records the full schema, exact split boundaries, ticker
order and volatility buckets, per-panel scalar values, canonical array
identities, and SHA-256 checksums.
## Loading
The close and volume configurations can be read independently:
from datasets import load_dataset
close = load_dataset("abel-lab/finance1k", "close")
volume = load_dataset("abel-lab/finance1k", "volume")
The synchronized arrays must remain intact when evaluating cross-sectional
metrics. Flattening ticker and timestamp axes changes the empirical task.
## Known limitations
The stored timeline is a regular calendar-day hourly grid rather than an
exchange-session calendar. Weekend and market-holiday rows are filled with zero
log changes.
The ticker universe is limited to names with sufficiently long historical
coverage to span the common evaluation window. Eligible tickers are divided
into ten historical-volatility buckets, with 100 selected from each bucket, to
balance the panel across volatility levels. This construction introduces
survivorship and selection effects and should not be interpreted as a
representative market sample.
## License
Finance1K is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
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