Presentation 2019-12-24
Quasi-Recurrent Neural Networksを用いた複合時系列データ予測
Yuichiro Sakazaki, Rin Adachi, Jun Rokui,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) We proposed a model that integrates several types of data by multiple regression analysis and performs future prediction of target using Quasi- Recurrent Neural Network, which is one of nonlinear models. In addition, we experiment to change the length of the time series data and the parameters of the nonlinear model. And the change of prediction accuracy is verified from the result.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) time-series / prediction / QRNN / machine-learning
Paper # DE2019-32
Date of Issue 2019-12-16 (DE)

Conference Information
Committee DE / IPSJ-DBS
Conference Date 2019/12/23(2days)
Place (in Japanese) (See Japanese page)
Place (in English) National Institute of Informatics
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Jun Miyazaki(Tokyo Inst. of Tech.) / 吉川 正俊(京大)
Vice Chair Shohei Yokoyama(Tokyo Metropolitan Univ.) / Kazuo Goda(Univ. of Tokyo)
Secretary Shohei Yokoyama(NTT) / Kazuo Goda(Univ. of Hyogo) / (筑波大)
Assistant Saneyasu Yamaguchi(Kogakuin Univ.) / Shoko Wakamiya(NAIST)

Paper Information
Registration To Technical Committee on Data Engineering / Special Interest Group on Database System
Language JPN-ONLY
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English)
Sub Title (in English)
Keyword(1) time-series
Keyword(2) prediction
Keyword(3) QRNN
Keyword(4) machine-learning
1st Author's Name Yuichiro Sakazaki
1st Author's Affiliation University of Shizuoka(univ. of Shizuoka)
2nd Author's Name Rin Adachi
2nd Author's Affiliation University of Shizuoka(univ. of Shizuoka)
3rd Author's Name Jun Rokui
3rd Author's Affiliation University of Shizuoka(univ. of Shizuoka)
Date 2019-12-24
Paper # DE2019-32
Volume (vol) vol.119
Number (no) DE-354
Page pp.pp.93-98(DE),
#Pages 6
Date of Issue 2019-12-16 (DE)