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- W4367694120 abstract "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." @default.
- W4367694120 created "2023-05-03" @default.
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- W4367694120 date "2023-04-29" @default.
- W4367694120 modified "2023-09-27" @default.
- W4367694120 title "Industry Classification Using a Novel Financial Time-Series Case Representation" @default.
- W4367694120 doi "https://doi.org/10.48550/arxiv.2305.00245" @default.
- W4367694120 hasPublicationYear "2023" @default.
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