The financial domain has proven to be a fertile source of challenging machine learning problems across a variety of tasks including prediction, clustering, and classification. Researchers can access an abundance of time-series data and even modest performance improvements can be translated into significant additional value. In this work, we consider the use of case-based reasoning for an important task in this domain, by using historical stock returns time-series data for industry sector classification. We discuss why time-series data can present some significant representational challenges for conventional case-based reasoning approaches, and in response, we propose a novel representation based on stock returns embeddings, which can be readily calculated from raw stock returns data. We argue that this representation is well suited to case-based reasoning and evaluate our approach using a large-scale public dataset for the industry sector classification task, demonstrating substantial performance improvements over several baselines using more conventional representations.

翻译：金融领域已被证明是机器学习中具有挑战性的问题（包括预测、聚类和分类等任务）的丰富来源。研究者可以获取大量时间序列数据，即使是微小的性能提升也能转化为显著附加值。本文针对该领域中的一项重要任务——利用历史股票收益率时间序列数据进行行业分类，探讨了案例推理方法的应用。我们分析了时间序列数据对传统案例推理方法构成的关键表征挑战，并据此提出了一种基于股票收益率嵌入的新型表示方法，该方法可直接从原始股票收益率数据计算得出。我们论证了该表示法对案例推理的适用性，并使用大规模公开数据集对行业分类任务进行评估。结果表明，与采用传统表示法的多个基线方法相比，本方法在性能上实现了显著提升。