The Properties of mode prediction using mean root error for regularization

Ghudae Sim; Hyungbin Yun; Junhee Seok

doi:10.1109/ICAIIC.2019.8669016

The Properties of mode prediction using mean root error for regularization

Ghudae Sim, Hyungbin Yun, Junhee Seok

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	509-511
Number of pages	3
ISBN (Electronic)	9781538678220
DOIs	https://doi.org/10.1109/ICAIIC.2019.8669016
Publication status	Published - 2019 Mar 18
Event	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan Duration: 2019 Feb 11 → 2019 Feb 13

Publication series

Name	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

Conference

Conference	1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Country/Territory	Japan
City	Okinawa
Period	19/2/11 → 19/2/13

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

Access to Document

10.1109/ICAIIC.2019.8669016

Cite this

Sim, G., Yun, H., & Seok, J. (2019). The Properties of mode prediction using mean root error for regularization. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 (pp. 509-511). Article 8669016 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC.2019.8669016

The Properties of mode prediction using mean root error for regularization. / Sim, Ghudae; Yun, Hyungbin; Seok, Junhee.
1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 509-511 8669016 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sim, G, Yun, H & Seok, J 2019, The Properties of mode prediction using mean root error for regularization. in 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019., 8669016, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 509-511, 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019, Okinawa, Japan, 19/2/11. https://doi.org/10.1109/ICAIIC.2019.8669016

Sim G, Yun H, Seok J. The Properties of mode prediction using mean root error for regularization. In 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 509-511. 8669016. (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019). doi: 10.1109/ICAIIC.2019.8669016

Sim, Ghudae ; Yun, Hyungbin ; Seok, Junhee. / The Properties of mode prediction using mean root error for regularization. 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 509-511 (1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019).

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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.",

keywords = "for predict ETF price, local optimal point, mean root error, mode prediction, non-convex optimization",

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AB - 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.

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The Properties of mode prediction using mean root error for regularization

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this