Learning summary features of time series for likelihood free inference

• URL: http://arxiv.org/abs/2012.02807v1
• Date: Fri, 4 Dec 2020 19:21:37 GMT
• Title: Learning summary features of time series for likelihood free inference
• Authors: Pedro L. C. Rodrigues, Alexandre Gramfort
• Abstract summary: We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
• Score: 93.08098361687722
• License: http://creativecommons.org/licenses/by/4.0/
• 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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