Fukui Shogo

Affiliate Master Yamaguchi University

A consideration of the prediction of input coefficient matrix with deep learning

山口経済学雑誌 Volume 70 Issue 1-2 Page 55-90

published_at 2021-07-31

Available 2025-07-31

[fulltext] 1.46 MB

Title

深層学習による投入係数行列の予測

A consideration of the prediction of input coefficient matrix with deep learning

Abstract

In this study, we present a new method for estimating input coefficient matrix with deep learning. We use the data of prefectures of Japan as the training data. However, the data is small and is likely to cause overfitting. Therefore, we attempt to avoid the over-fitting by creating new data of virtual areas aggregating multiple prefectures. The approach is based on the concept of data augmentation. As a result of predicting the input coefficient matrix of Yamaguchi prefecture and Gujo city, we showed that the method using deep learning can estimate the matrix with more stable accuracy than RAS method for these areas.

Creators Fukui Shogo

Source Identifiers [PISSN] 0513-1758 [NCID] AN00243258

Creator Keywords

深層学習産業連関表投入係数 Deep Learning Input-Output Table Input Coefficient

Languages jpn

Resource Type departmental bulletin paper

Publishers 山口大學經濟學會

Date Issued 2021-07-31

File Version Version of Record

Access Rights embargoed access

Relations

[ISSN]0513-1758

[NCID]AN00243258

Schools 経済学部