| ---
|
| 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:
|
| - time-series
|
| - forecasting
|
| - benchmark
|
| - fev
|
| - tsfile
|
| - apache-tsfile
|
| - modality:timeseries
|
| - Time-series
|
| - format:tsfile
|
| - arxiv:2509.26468
|
| size_categories:
|
| - 100K<n<1M
|
| pretty_name: m5 (TsFile format)
|
| configs:
|
| - config_name: default
|
| data_files:
|
| - split: train
|
| path: "**/*.tsfile"
|
| ---
|
|
|
| # 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
|
| ```
|
|
|
