  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.
