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| license: apache-2.0 | |
| pretty_name: skycmd synthetic time series (s6-cast generators) | |
| task_categories: | |
| - time-series-forecasting | |
| tags: | |
| - skycmd | |
| configs: | |
| - config_name: gp | |
| data_files: data/gp.parquet | |
| - config_name: mixed | |
| data_files: data/mixed.parquet | |
| - config_name: pulse | |
| data_files: data/pulse.parquet | |
| - config_name: sarima | |
| data_files: data/sarima.parquet | |
| - config_name: tsi | |
| data_files: data/tsi.parquet | |
| # skycmd synthetic time series (s6-cast generators) | |
| A fixed sample of the four synthetic series generators that pretrain the s6-cast forecaster (Gaussian processes, pulses, trend-seasonality-irregular, SARIMA), and of their training mix. Generated by skycmd's own Rust code: no third-party data. | |
| Part of [skycmd](https://github.com/maxgfr/skycmd), tiny decision models trained from scratch in Rust that run in the | |
| browser. Written by `sky-train cast-synth --kind <family> --n 1000 --len 2048 --seed 1, and --n 4000 for the mix` at commit [`8cc8e276`](https://github.com/maxgfr/skycmd/commit/8cc8e276adabbe1cc37998fb70fdd5c947ffda81). | |
| Licences of every set: [DATA_LICENSES.md](https://github.com/maxgfr/skycmd/blob/main/DATA_LICENSES.md). | |
| Each row is one synthetic series: `id` (`<family>-<i>`) and `values` (2,048 floats). The generators | |
| are ours (`crates/sky-infer/src/cast/synth.rs`), after Reverso (arXiv 2602.17634, appendix B) and | |
| Simulation Alone (arXiv 2601.00970), and deterministic per seed. s6-cast does not train on these | |
| rows: it trains on an endless stream from the same generators (a refreshed pool, other seeds). | |
| This repository is a fixed sample of that stream (`sky-train cast-synth --seed 1 --len 2048`), to | |
| inspect the generators or to pretrain another forecaster on the same distribution. | |
| ## 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 | | |
| |---|---|---|---|---:| | |
| | `gp` | ours: `sky_infer::cast::synth`, family `gp` | apache-2.0 | KernelSynth-style Gaussian-process draws: 1 to 5 kernels (constant, linear, RBF, rational quadratic, Matérn, periodic) combined by sum or product, sampled with random Fourier features | 1,000 | | |
| | `mixed` | ours: the training mix | apache-2.0 | the four families drawn with weights 0.3 / 0.15 / 0.3 / 0.25, as in s6-cast training | 4,000 | | |
| | `pulse` | ours: family `pulse` | apache-2.0 | trapezoid pulse trains (spikes or dips) over a baseline, with noise | 1,000 | | |
| | `sarima` | ours: family `sarima` | apache-2.0 | seasonal ARIMA with stationary AR parts (sampled through their partial autocorrelations), optional differencing | 1,000 | | |
| | `tsi` | ours: family `tsi` | apache-2.0 | trend (linear, exponential, polynomial, piecewise) + up to 3 seasonal waves + Gaussian, Laplace or Student-t noise + outliers + level shifts | 1,000 | | |
| ## Files | |
| | file | rows | | |
| |---|---:| | |
| | `data/gp.parquet` | 1,000 | | |
| | `data/mixed.parquet` | 4,000 | | |
| | `data/pulse.parquet` | 1,000 | | |
| | `data/sarima.parquet` | 1,000 | | |
| | `data/tsi.parquet` | 1,000 | | |
| ```python | |
| from datasets import load_dataset | |
| ds = load_dataset("maxgfr/skycmd-cast-synth", "gp") | |
| ``` | |
| 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. | |