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Learning summary features of time series for likelihood free inference

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.
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Contributor : Pedro Luiz Coelho Rodrigues Connect in order to contact the contributor
Submitted on : Wednesday, October 6, 2021 - 11:01:02 AM
Last modification on : Saturday, June 25, 2022 - 8:29:52 PM
Long-term archiving on: : Friday, January 7, 2022 - 6:38:22 PM


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  • HAL Id : hal-03367439, version 1


Pedro Luiz Coelho Rodrigues, Alexandre Gramfort. Learning summary features of time series for likelihood free inference. Workshop on Machine Learning and the Physical Sciences at the 34th conference on NeurIPS, Dec 2020, Online conference, United States. ⟨hal-03367439⟩



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