Interpretable model-free inference of parametric variation across time-series data through large-scale feature extraction
通过大规模特征提取解释时间序列中的参数变化
Here we address the problem of estimating the dimensionality and nature of parametric variation in an unknown generative process directly from time-series data, without specifying or fitting a model. In particular we suppose that inter-instance variation in collections of time series is caused by parametric variation in the generating model. We hypothesize that, given a sufficiently large library of time-series features, low-dimensional parametric variation will manifest as…