Datasets:
Tasks:
Time Series Forecasting
Modalities:
Text
Formats:
parquet
Size:
10K - 100K
Tags:
skycmd
License:
| license: other | |
| license_name: per-source | |
| license_link: https://github.com/maxgfr/skycmd/blob/main/DATA_LICENSES.md | |
| pretty_name: skycmd time series (s6-cast training) | |
| task_categories: | |
| - time-series-forecasting | |
| tags: | |
| - skycmd | |
| configs: | |
| - config_name: games | |
| data_files: data/games.parquet | |
| - config_name: london_smart_meters | |
| data_files: data/london_smart_meters.parquet | |
| - config_name: pedestrian_counts | |
| data_files: data/pedestrian_counts.parquet | |
| - config_name: rideshare | |
| data_files: data/rideshare.parquet | |
| - config_name: ushcn_daily | |
| data_files: data/ushcn_daily.parquet | |
| - config_name: wiki_daily | |
| data_files: data/wiki_daily.parquet | |
| - config_name: wind_farms_daily | |
| data_files: data/wind_farms_daily.parquet | |
| - config_name: wind_farms_hourly | |
| data_files: data/wind_farms_hourly.parquet | |
| # skycmd time series (s6-cast training) | |
| The training series of the s6-cast forecaster: public series absent from GIFT-Eval and Chronos' zero-shot benchmark, and the state variables of the skycmd games. | |
| Part of [skycmd](https://github.com/maxgfr/skycmd), tiny decision models trained from scratch in Rust that run in the | |
| browser. Written by `sky-data fetch-series and sky-train cast-synth --games` at commit [`84c6d854`](https://github.com/maxgfr/skycmd/commit/84c6d854cdc2a9f6d951ebb55780060a01f38ca6). | |
| Licences of every set: [DATA_LICENSES.md](https://github.com/maxgfr/skycmd/blob/main/DATA_LICENSES.md). | |
| Each row is one series: `id` (`<source>:<series>`) and `values` (a list of floats, in time order). | |
| The s6-cast trainer holds out one series in ten, by id, for validation; windows and synthetic | |
| series (`sky-train cast-synth`) are drawn from these at training time. | |
| ## Sources and licences | |
| Each config is one source and keeps **its source's licence** (share-alike sources stay share-alike). | |
| Attribution goes to the upstream authors; the conversion is ours. | |
| | config | upstream | licence | how it is used | rows | | |
| |---|---|---|---|---:| | |
| | `games` | ours: state variables of the skycmd games (`sky-train cast-synth --games`) | apache-2.0 | 60 oracle episodes per game | 1,549 | | |
| | `london_smart_meters` | autogluon/chronos_datasets `monash_london_smart_meters` (Monash archive, Godahewa et al. 2021) | cc-by-4.0 | first 2,000 series, last 8,192 points | 2,000 | | |
| | `pedestrian_counts` | autogluon/chronos_datasets `monash_pedestrian_counts` (Monash archive, Godahewa et al. 2021) | cc-by-4.0 | all series | 66 | | |
| | `rideshare` | autogluon/chronos_datasets `monash_rideshare` (Monash archive, Godahewa et al. 2021) | cc-by-4.0 | every numeric column of the first 200 rows | 200 | | |
| | `ushcn_daily` | autogluon/chronos_datasets `ushcn_daily` (NOAA NCEI) | public domain (U.S. Government work) | every numeric column of the first 1,300 rows | 1,300 | | |
| | `wiki_daily` | autogluon/chronos_datasets `wiki_daily_100k` (Wikimedia pageviews) | cc0-1.0 | first 5,000 series | 5,000 | | |
| | `wind_farms_daily` | autogluon/chronos_datasets `wind_farms_daily` (Monash archive, Godahewa et al. 2021) | cc-by-4.0 | all series, last 8,192 points | 337 | | |
| | `wind_farms_hourly` | autogluon/chronos_datasets `wind_farms_hourly` (Monash archive, Godahewa et al. 2021) | cc-by-4.0 | all series, last 8,192 points | 337 | | |
| ## Files | |
| | file | rows | | |
| |---|---:| | |
| | `data/games.parquet` | 1,549 | | |
| | `data/london_smart_meters.parquet` | 2,000 | | |
| | `data/pedestrian_counts.parquet` | 66 | | |
| | `data/rideshare.parquet` | 200 | | |
| | `data/ushcn_daily.parquet` | 1,300 | | |
| | `data/wiki_daily.parquet` | 5,000 | | |
| | `data/wind_farms_daily.parquet` | 337 | | |
| | `data/wind_farms_hourly.parquet` | 337 | | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("maxgfr/skycmd-series", "games") | |
| ``` | |
| skycmd wrote no row with a language model: the targets come from the upstream labels (some upstream | |
| sets carry their own teacher soft labels, see their cards), the games' oracles or our generators. | |