[3D5-GS-2] Machine learning: Time series

Thu. May 30, 2024 3:30 PM - 5:10 PM Room D (Temporary room 2)

座長：吉田周平（NEC）[[オンライン]]

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3:30 PM - 3:50 PM

[3D5-GS-2-01] Leveraging Latent Space Symmetry for Time Series Prediction

〇Seong Cheol Jeong¹, Yuki Goto^2,4, Kei Tsukamoto¹, Makoto Kawano¹, Akiyoshi Sannai³, Yutaka Matsuo¹, Wataru Kumagai^1,4 (1. The University of Tokyo, 2. Keio University, 3. Kyoto University, 4. RIKEN AIP)

3:50 PM - 4:10 PM

[3D5-GS-2-02] An evaluation and comparison of time series clustering using technical indices

〇Tohgoroh Matsui¹, Yoshiki Nakagawa², Koichi Moriyama², Kosuke Shima², Atsuko Mutoh², Nobuhiro Inuzuka² (1. Chubu University, 2. Nagoya Institute of Technology)

4:10 PM - 4:30 PM

[3D5-GS-2-03] Which time periods does the ROCKET model focus on for each inference?

〇Takuji Oba¹, Akihiro Shiozawa¹ (1. NTT DATA Mathematical Systems Inc.)

4:30 PM - 4:50 PM

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[3D5-GS-2-04] Structure estimation of the hierarchical networks and its application to anomaly detection

Anomaly detection of financial market and cause analysis through hierarchical structure analysis of financial time series data

〇NAIJIA LIU¹, Yukio Ohsawa¹, Kaira Sekiguchi¹, Takaaki Yoshino², Toshiaki Sugie², Yoshiyuki Nakata², Kakeru Ito² (1. University of Tokyo, 2. Nissay Asset Management Corporation)

4:50 PM - 5:10 PM

[3D5-GS-2-05] Residual return extraction using Principal Component Equivalence method

〇Kentaro Imajo¹, Kei Nakagawa², Kazuki Matoya¹, Masanori Hirano¹, Masana Aoki², Taku Imahase² (1. Preferred Networks, Inc., 2. Nomura Asset Management Co., Ltd.)

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