README.md

metadata

license: cc-by-4.0
pretty_name: KernelSynth (annotated)
task_categories:
  - time-series-forecasting
size_categories:
  - 1M<n<10M
tags:
  - synthetic
  - gaussian-process
  - kernel-synth
  - interpretability
dataset_info:
  features:
    - name: start
      dtype: timestamp[s]
    - name: target
      sequence: float64
    - name: selected_kernel_reprs
      sequence: string
    - name: kernel_formula
      dtype: string
  splits:
    - name: train
      num_bytes: 8478398125
      num_examples: 1000000
  download_size: 8493914077
  dataset_size: 8478398125
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

KernelSynth (annotated)

One million synthetic univariate time series, each 1024 points long, drawn from a Gaussian process prior whose kernel is a random composition of up to five base kernels. This is the KernelSynth procedure from Chronos with one addition: the generating kernel is kept alongside each series. The ground-truth structure behind every series is therefore known, which makes the corpus usable for interpretability work rather than only for pretraining.

Fields

• start — constant 2000-01-01T00:00:00. The time index is arbitrary and carries no meaning.
• target — the series itself: 1024 float64 values sampled from the GP prior.
• selected_kernel_reprs — the base kernels drawn from the kernel bank, as scikit-learn reprs.
• kernel_formula — the composed kernel with explicit precedence, e.g. RBF(length_scale=1) * (DotProduct(sigma_0=1) + WhiteKernel(noise_level=1)). scikit-learn's own repr omits parentheses, so (a + b) * c and a + b * c are indistinguishable there; this field disambiguates them.

Generation

Produced by a script adapted from Chronos' kernel-synth.py, with these settings: 1,000,000 series, length 1024, at most 5 base kernels each, seed 1. Kernels are drawn with replacement from the 33-entry Chronos kernel bank and combined pairwise with random + / * operators. Each series draws from its own independent random stream derived from the seed, so the corpus is reproducible and independent of worker count.

One deviation from upstream: a jitter of 1e-8 * mean(diag(cov)) is added to the covariance diagonal to keep it numerically positive semi-definite, since composed kernels are often ill-conditioned.

License and attribution

The data is released under CC-BY-4.0.

It was produced by a script adapted from Chronos' kernel-synth.py (Copyright Amazon.com, Inc., Apache-2.0). That license covers the generator code, not the series it emits, and no Chronos data is contained here — every series is sampled fresh from a GP prior. The method, however, is theirs; please cite the Chronos (1) paper.