| Project/Area Number |
18K04626
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 25010:Social systems engineering-related
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| Research Institution | Kurume University |
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Principal Investigator |
Tan Kouyuu 久留米大学, 経済学部, 教授 (70368968)
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| Co-Investigator(Kenkyū-buntansha) |
谷口 剛 久留米大学, 文学部, 教授 (00102096)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | Change Point / State/Structural Change / Bayesian Inference / Markov Regime Switching / SDE Dynamics / KSVM / Change point / Anomaly detection / Parallel computing / Baysian Inference / MRSA / Support Vector Machine / SDE / GEV distribution / Structural Change / change point / Bayesian / jump / particle filter / 構造変化・転換点の検出 / ジャンプ / 形状変化の推定 / 確率分布の推定 / ネットワークの変化 / 転換点の検出 / 構造変化の検出 / 確率微分方程式 / 不正侵入の検出 / ネットワークトラフィック / 数理統計的なアプローチ / 非線形確率システム / 状態・構造変化の検知、予測 / リスク管理 / ハイブリッド解析法 |
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| Outline of Final Research Achievements |
The following results were obtained from this research:
The multiple methods proposed, established, and developed in this research (such as mathematical modeling techniques and system analysis methods) have been confirmed through empirical analysis and numerical analysis, including simulations, to be effective and applicable in various fields. These fields include not only the analysis of economic and management time series data, but also the detection of structural changes in stochastic differential equations (SDEs), the detection of network attacks on systems, and signal processing of time series data. Additionally, the use of parallel computing with multicore technology has allowed for a significant exponential reduction in computation time for methods such as Markov Chain Monte Carlo Methods. International collaboration and research partnerships enabled the dissemination of Japan's research achievements to the world.
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| Academic Significance and Societal Importance of the Research Achievements |
非線形確率システムにおける状態変化・構造変化に関する既往の成果よりも,本研究では,より汎用的な数理モデリング/解析手法を複数に提案・開発しており, より正確的にリアルタイムにシステムの状態変化・構造変化を検出することができた。
提案手法はシステムの構造変化, COVID-19の感染者数の動的変化, ネット攻撃, 株価・為替レートの急激な変化などをいちはやく捉えることができた。文理を問わず,多くの分野における提案・開発された手法の有効性が実証分析・数値解析によって確認された。これからのより情報化・AI化の社会において、様々な状況において,提案手法の活用が期待できると考えられる。
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