---
license: other
task_categories:
- time-series-forecasting
task_ids:
- univariate-time-series-forecasting
- multivariate-time-series-forecasting
annotations_creators:
- no-annotation
source_datasets:
- original
tags:
- forecasting
- benchmark
- fev
- arxiv:2509.26468
- tsfile
- modality:timeseries
- timeseries
- format:tsfile
size_categories:
- 10K<n<100K
pretty_name: m5 (TsFile format)
configs:
- config_name: default
  data_files:
  - split: train
    path: "**/*.tsfile"
modality:
- timeseries
---

# m5 (TsFile format)

This repository contains time-series forecasting data stored in [Apache TsFile](https://tsfile.apache.org/) format.

## Summary

- FEV subset: `m5`
- Unified source collection: [`autogluon/fev_datasets`](https://huggingface.co/datasets/autogluon/fev_datasets)
- Original source: https://www.kaggle.com/competitions/m5-forecasting-accuracy
- Paper / citation: [[15]](https://doi.org/10.1016/j.ijforecast.2021.11.013)
- Series: 30,490
- Modalities: Time-series
- TsFile rows (flattened observations): 503,512,848
- Frequencies: 1D, 1M, 1W
- TsFile files: 55
- Time precision: milliseconds (`INT64`).

Licensing and citation requirements follow the original source. This repository does not claim ownership of the original data.

## Dataset Statistics

| Frequency | Series | Median series length | TsFile rows (observations) | Dynamic columns | Static columns | Data files |
|---|---:|---:|---:|---:|---:|---|
| 1D | 30,490 | 1,810 | 428,849,460 | 9 | 5 | `1D/1D_1..1D_46.tsfile` (46 shards) |
| 1M | 30,490 | 58 | 13,805,685 | 9 | 5 | `1M/1M_1..1M_2.tsfile` (2 shards) |
| 1W | 30,490 | 257 | 60,857,703 | 9 | 5 | `1W/1W_1..1W_7.tsfile` (7 shards) |

## Files

The Hugging Face dataset card YAML points `configs.data_files` to all `*.tsfile` files in this repository.

- `1D/1D_1.tsfile`
- `1D/1D_10.tsfile`
- `1D/1D_11.tsfile`
- `1D/1D_12.tsfile`
- `1D/1D_13.tsfile`
- `1D/1D_14.tsfile`
- `1D/1D_15.tsfile`
- `1D/1D_16.tsfile`
- `1D/1D_17.tsfile`
- `1D/1D_18.tsfile`
- `1D/1D_19.tsfile`
- `1D/1D_2.tsfile`
- `1D/1D_20.tsfile`
- `1D/1D_21.tsfile`
- `1D/1D_22.tsfile`
- `1D/1D_23.tsfile`
- `1D/1D_24.tsfile`
- `1D/1D_25.tsfile`
- `1D/1D_26.tsfile`
- `1D/1D_27.tsfile`
- ... 35 more `.tsfile` files

## TsFile Storage Model

- Each original series (`id`) is stored as one TsFile device.
- Static covariate columns are stored as TAG columns: `item_id, dept_id, cat_id, store_id, state_id`.
- Time-varying targets and dynamic covariates are stored as FIELD measurements.
- Source `timestamp` values are mapped to the TsFile `Time` column as millisecond timestamps.
- Table name(s): m5_1D, m5_1M, m5_1W.

### Column Schema

| Column | Role | TsFile type |
|---|---|---|
| `Time` | Time column | INT64 |
| `id` | TAG (device dimension) | STRING |
| `item_id` | TAG (device dimension) | STRING |
| `dept_id` | TAG (device dimension) | STRING |
| `cat_id` | TAG (device dimension) | STRING |
| `store_id` | TAG (device dimension) | STRING |
| `state_id` | TAG (device dimension) | STRING |
| `target` | FIELD (measurement) | FLOAT |
| `snap_CA` | FIELD (measurement) | BOOLEAN |
| `snap_TX` | FIELD (measurement) | BOOLEAN |
| `snap_WI` | FIELD (measurement) | BOOLEAN |
| `sell_price` | FIELD (measurement) | FLOAT |
| `event_Cultural` | FIELD (measurement) | BOOLEAN |
| `event_National` | FIELD (measurement) | BOOLEAN |
| `event_Religious` | FIELD (measurement) | BOOLEAN |
| `event_Sporting` | FIELD (measurement) | BOOLEAN |

## Conversion Notes

- The source FEV format stores each time series as one nested row containing `id`, `timestamp[]`, and target or covariate arrays.
- The TsFile conversion flattens those nested arrays into long rows. Therefore, the `TsFile rows` values above correspond to the number of timestamped observations after flattening.
- TAG columns identify the device and static metadata. FIELD columns contain values that change over time.
- Large logical tables may be split into multiple `.tsfile` shards such as `<name>_1.tsfile`, `<name>_2.tsfile`, and so on. Shards listed for the same frequency belong to the same logical table.

## Reading Example

```python
from tsfile import TsFileReader

reader = TsFileReader("1D/1D_1.tsfile")
schemas = reader.get_all_table_schemas()
# Table name(s): m5_1D, m5_1M, m5_1W
```