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<p>In financial time series classification, concept drift adaptation is crucial to maintain model performance, as data distributions evolve over time. To handle concept drift, there are mainly two methods: detection-based, which uses historical data, and non-detection-based, which relies on more immediate, smaller volume real-time data. The former preserves historical patterns but comparatively exhibits hysteresis, while the latter is opposition. Thus, it remains a challenge to achieve continuous model updating while preserving historical patterns. To address this issue, we propose a novel non-detection-based method. This method retains valuable historical patterns and achieves continuous model updating by observing distribution variations, which simultaneously controls different hyperparameters on a data. Specifically, we develop the Adaptive Financial Time Series Classification Model (AFinSeqClass), which integrates the Complementary Margin Support Vector Machine (C-Margin SVM) and the Inverse Derivation Algorithm (InvDA) to handle sample and feature selection. For sample selection, C-Margin SVM is a dual-distribution approach that skillfully utilizes soft and hard margin theories to generate two distinct data distributions from one dataset. These two distributions generate a dynamic signal-to-noise ratio margin—complementary margin, which is the set difference between the soft margin and hard margin. We then select low-noise and information-rich samples from the complementary margin. For feature selection, InvDA reverses the forward derivation of financial features using cooperative co-evolution strategies to break down complex problems into smaller sub-problems, corresponding to homologous feature groups to gradually refine the feature set, minimizing the redundancy among features. Through the cooperation of these two algorithms, across a series of stocks, the AFinSeqClass model achieves a classification accuracy exceeding 60%. The implementation code of the AFinSeqClass model is available at https://github.com/MultiPaperCode/Adaptive-Financial-Time-Series-Classification.git.</p>

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Adaptive Financial Time Series Classification: Leveraging Historical Samples from Signal-to-Noise Ratio Margins

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • ZhiPeng Jiang,
  • Hua Zou,
  • Dengyi Zhang,
  • Qian Cai,
  • Yu Li,
  • Fuyong Liu,
  • Xueli Qin

摘要

In financial time series classification, concept drift adaptation is crucial to maintain model performance, as data distributions evolve over time. To handle concept drift, there are mainly two methods: detection-based, which uses historical data, and non-detection-based, which relies on more immediate, smaller volume real-time data. The former preserves historical patterns but comparatively exhibits hysteresis, while the latter is opposition. Thus, it remains a challenge to achieve continuous model updating while preserving historical patterns. To address this issue, we propose a novel non-detection-based method. This method retains valuable historical patterns and achieves continuous model updating by observing distribution variations, which simultaneously controls different hyperparameters on a data. Specifically, we develop the Adaptive Financial Time Series Classification Model (AFinSeqClass), which integrates the Complementary Margin Support Vector Machine (C-Margin SVM) and the Inverse Derivation Algorithm (InvDA) to handle sample and feature selection. For sample selection, C-Margin SVM is a dual-distribution approach that skillfully utilizes soft and hard margin theories to generate two distinct data distributions from one dataset. These two distributions generate a dynamic signal-to-noise ratio margin—complementary margin, which is the set difference between the soft margin and hard margin. We then select low-noise and information-rich samples from the complementary margin. For feature selection, InvDA reverses the forward derivation of financial features using cooperative co-evolution strategies to break down complex problems into smaller sub-problems, corresponding to homologous feature groups to gradually refine the feature set, minimizing the redundancy among features. Through the cooperation of these two algorithms, across a series of stocks, the AFinSeqClass model achieves a classification accuracy exceeding 60%. The implementation code of the AFinSeqClass model is available at https://github.com/MultiPaperCode/Adaptive-Financial-Time-Series-Classification.git.