From Music Information Retrieval to Stock Market Analysis: Theoretical Discussion on Feature Extraction Transfer

Hanchao Li, Xiang Fei, Ming Yang, Kuo-Ming Chao, Chaobo He

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Finding similar objects and patterns based on the similarity score is one of the fundamental and useful tasks in Data Mining. Different applications may introduce different features that need to be extract and analyzed. However, if two applications share some similar core concepts, it is possible to transfer some features that will be beneficial to transfer learning. Thus, this paper uses some features from Music Information Retrieval and Stock Market Analysis to theoretically illustrate the possibility of Feature Extraction Transfer. We use a 3-tuple or 6-tuple vector to record the music fundamental melody whereas a 5-tuple vector to record the daily behavior of the stock market from the candlestick chart. Hence, the flow of one music melody and the flow of one stock market can be treat as a time series vector sequence. Using this linkage, we have computed some feature exaction from Music Information Retrieval onto Stock Market Analysis and obtained some positive results. For example, the similarity between Activision Blizzard Inc and Zynga Inc have achieved a similarity score of 0.6250. Moreover, these positive results gave some ideas on implementing a self-supervised learning based system to manage your stock market and the potential of implementing a transfer learning between these two applications.
Original languageEnglish
Title of host publication2021 IEEE International Conference on e-Business Engineering (ICEBE)
PublisherIEEE
Pages54-58
Number of pages5
ISBN (Electronic)978-1-6654-4418-7
ISBN (Print)978-1-6654-4419-4
DOIs
Publication statusPublished - 11 Apr 2021
EventIEEE International Conference on e-Business Engineering 2021 - Virtual
Duration: 12 Nov 202114 Nov 2021

Publication series

Name2021 IEEE International Conference on e-Business Engineering (ICEBE)
PublisherIEEE

Conference

ConferenceIEEE International Conference on e-Business Engineering 2021
Period12/11/2114/11/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported in part by the Natural Science Foundation of Guangdong Province of China under Grant 2019A1515011292, and in part by the Science and Technology Support Program of Guangzhou City of China under Grant 201905010006.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Data Mining
  • Feature Extraction
  • Feature Transfer Music Information Retrieval
  • Stock Market Analysis
  • Transfer Learning

ASJC Scopus subject areas

  • Business and International Management
  • Marketing
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management

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