论文标题
学习时间序列的摘要特征免费推断
Learning summary features of time series for likelihood free inference
论文作者
论文摘要
科学界对使用无似然推理（LFI）来确定给定模拟器模型的哪些参数可以最好地描述一组实验数据的兴趣。尽管最近的结果和广泛的应用程序令人兴奋，但应用于时间序列数据时，LFI的重要瓶颈是定义一组摘要功能的必要性，这些功能通常基于域知识进行手动尾部。在这项工作中，我们提出了一种数据驱动的策略，用于从单变量时间序列中自动学习摘要特征，并将其应用于自动回忆 - 运动平均（ARMA）模型和Van der Pol振荡器的信号。我们的结果表明，从数据中学习的摘要功能也可以竞争，甚至在线性情况下，基于手工创建的值（例如自相关系数），甚至超过了LFI方法，例如自相关系数。
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.
