English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The financial domain has proven to be a fertile source of challenging machinelearning problems across a variety of tasks including prediction, clustering,and classification. Researchers can access an abundance of time-series data andeven modest performance improvements can be translated into significantadditional value. In this work, we consider the use of case-based reasoning foran important task in this domain, by using historical stock returns time-seriesdata for industry sector classification. We discuss why time-series data canpresent some significant representational challenges for conventionalcase-based reasoning approaches, and in response, we propose a novelrepresentation based on stock returns embeddings, which can be readilycalculated from raw stock returns data. We argue that this representation iswell suited to case-based reasoning and evaluate our approach using alarge-scale public dataset for the industry sector classification task,demonstrating substantial performance improvements over several baselines usingmore conventional representations.

Industry Classification Using a Novel Financial Time-Series Case Representation