The Properties of mode prediction using mean root error for regularization

Ghudae Sim, Hyungbin Yun, Junhee Seok

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Citations (Scopus)

    Abstract

    While it is popular, estimating empirical distribution from observed data using MSE (Mean Squared Error) is often inefficient because it focuses on expectation. To address this problem, here we invest a new type of error term, named MRE (Mean Root Error). Different from MSE, MRE can predict the local mode point rather than the expectation. From numerical studies, we show that MRE models shows more robust and accurate prediction performance, which will be useful for complicated data such as finance data.

    Original languageEnglish
    Title of host publication1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages509-511
    Number of pages3
    ISBN (Electronic)9781538678220
    DOIs
    Publication statusPublished - 2019 Mar 18
    Event1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan
    Duration: 2019 Feb 112019 Feb 13

    Publication series

    Name1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

    Conference

    Conference1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
    Country/TerritoryJapan
    CityOkinawa
    Period19/2/1119/2/13

    Bibliographical note

    Funding Information:
    This work was supported by the National Research Foundation of Korea grant (NRF-2017R1C1B2002850) and Korea University grant (K1822271) as well as a grant from Mirae Asset Global Investments. Correspondence should be addressed to [email protected].

    Publisher Copyright:
    © 2019 IEEE.

    Keywords

    • for predict ETF price
    • local optimal point
    • mean root error
    • mode prediction
    • non-convex optimization

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Computer Science Applications
    • Artificial Intelligence

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