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.
Cite
@article{arxiv.2012.02807,
title = {Learning summary features of time series for likelihood free inference},
author = {Pedro L. C. Rodrigues and Alexandre Gramfort},
journal= {arXiv preprint arXiv:2012.02807},
year = {2020}
}