Presentation information

General Session

General Session » GS-10 AI application

[1J2-GS-10d] AI応用:分類と論理

Tue. Jun 8, 2021 1:20 PM - 3:00 PM Room J (GS room 5)

座長:曽我 真人(和歌山大学)

2:00 PM - 2:20 PM

[1J2-GS-10d-03] A study of multimodalization of spatio-temporal neural network for prediction

〇Yutaro Mishima1,2, Shinya Wada1 (1. KDDI Research, Inc., 2. KDDI Corporation)

Keywords:Spatio-Temporal Data, Multimodal, Deep Learning, Air quality, Forecasting

These days, many novel neural networks for modeling spatio-temporal relationship are proposed as many kinds of spatio-temporal datasets like location dataset or traffic dataset are published and utilized. However, novel networks have a common problem that they cannot handle properly multimodal data with complex (multi-step) relationship, e.g. Modal A affects modal B and modal B affects modal C. This problem must be solved because much more kinds of spatio-temporal data will be distributed in the future. In this paper, for discussing what kind of structure “multimodal spatio-temporal network” should be, we conduct some preliminary experiments which includes extending existing spatio-temporal network to handle multimodal data and comparing prediction capability with the original network. Based on the results, we conclude that “multimodal spatio-temporal network” should properly encode the information which affects relationship of modals dynamically, e.g. meteorological data.

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