This paper introduces an algorithm-agnostic approach to feature-based time series clustering via amortized neural inference. By training neural networks to approximate the optimal partitioning rule from simulated data, the proposed framework reduces reliance on conventional clustering methods, such as $K$-means, $K$-medoids, or hierarchical clustering, and their associated objective functions and heuristics. Leveraging statistical features, such as autocorrelations and quantile autocorrelations, the approach learns a data-driven affinity structure from which clustering partitions can be recovered, without requiring explicit prior specification of cluster shapes or structures. In addition, one version of the method can automatically determine the number of clusters, avoiding ad-hoc selection procedures. Comprehensive empirical studies show that the proposed framework achieves competitive or superior clustering accuracy relative to traditional methods, even in challenging scenarios where competing techniques are provided with the true number of clusters. An application to financial time series of stock returns illustrates its practical utility. By reducing the need for algorithm selection and calibration, the proposed framework opens new possibilities for automated, adaptive, and data-driven clustering of temporal data across scientific and industrial domains.

翻译：本文提出了一种基于摊销式神经推理的无算法依赖性特征时间序列聚类框架。通过训练神经网络从模拟数据中逼近最优划分规则，该框架减少了对传统聚类方法（如$K$-均值、$K$-中心点或层次聚类）及其相关目标函数与启发式算法的依赖。该方法利用自相关、分位数自相关等统计特征，学习一种数据驱动的亲和结构，进而从中恢复聚类划分，无需预先明确指定聚类形状或结构。此外，该方法的某个变体能够自动确定聚类数量，从而避免采用临时性的选择程序。大量实证研究表明，相较于传统方法，该框架即使在对竞争方法提供真实聚类数的挑战性场景中，也能实现具有竞争力或更优的聚类精度。将其应用于股票收益的金融时间序列数据，进一步验证了其实用价值。通过减少算法选择与参数调优的需求，该框架为科学和工业领域中的时序数据自适应、自动化聚类开辟了新途径。