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There has been an increasing interest from the scientific community in usinglikelihood-free inference (LFI) to determine which parameters of a givensimulator model could best describe a set of experimental data. Despiteexciting recent results and a wide range of possible applications, an importantbottleneck of LFI when applied to time series data is the necessity of defininga set of summary features, often hand-tailored based on domain knowledge. Inthis work, we present a data-driven strategy for automatically learning summaryfeatures from univariate time series and apply it to signals generated fromautoregressive-moving-average (ARMA) models and the Van der Pol Oscillator. Ourresults indicate that learning summary features from data can compete and evenoutperform LFI methods based on hand-crafted values such as autocorrelationcoefficients even in the linear case.

Learning summary features of time series for likelihood free inference