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Learning summary features of time series for likelihood free inference

PLC Rodrigues, A Gramfort - arXiv preprint arXiv:2012.02807, 2020 - arxiv.org
Statistics paper stat.ML Suggest

… This approach is also called simulation-based inference (SBI) and we refer the reader to [5] for a review on the topic and the Python package sbi for ready-to-use implementations [18]. …

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BibTeX

@article{2012.02807v1,
Author        = {Pedro L. C. Rodrigues and Alexandre Gramfort},
Title         = {Learning summary features of time series for likelihood free inference},
Eprint        = {2012.02807v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {stat.ML},
Abstract      = {There has been an increasing interest from the scientific community in using likelihood-free inference (LFI) to determine which parameters of a given simulator model could best describe a set of experimental data. Despite exciting recent results and a wide range of possible applications, an important bottleneck of LFI when applied to time series data is the necessity of defining a set of summary features, often hand-tailored based on domain knowledge. In this work, we present a data-driven strategy for automatically learning summary features from univariate time series and apply it to signals generated from autoregressive-moving-average (ARMA) models and the Van der Pol Oscillator. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values such as autocorrelation coefficients even in the linear case.},
Year          = {2020},
Month         = {Dec},
Url           = {http://arxiv.org/abs/2012.02807v1},
File          = {2012.02807v1.pdf}
}

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