{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-utility-of-hyperplane-angle-metric-in","title":"The Utility of Hyperplane Angle Metric in Detecting Financial Concept Drift","arxiv_id":null,"date":"2025-01-12","proceeding":"Applied Intelligence 2025 1","authors":["ZhiPeng"],"abstract":"In financial time series analysis, introducing a new metric for concept drift is essential to address the limitations of existing evaluation methods, particularly in terms of speed, interpretability and stability. Performance-based metrics and model-based metrics are the most commonly used to detect concept drift. For example, the error rate, which belongs to performance-based metric, is a frequently used metric that directly reflects the difference between the model's output and the actual results, making it suitable for quick decision-making. Mahalanobis Distance, being a model-based metric, detects concept drift by evaluating deviations in the sample distribution, offering deeper interpretability and stability. Generally speaking, performance-based metrics excel in speed but lack deeper interpretability and stability, while model-based metrics are opposite. To achieve speed, deeper interpretability and stability, we propose a novel metric termed the Angle Between Hyperplanes (ABH), which calculates the angle between earlier and later hyperplanes at two distinct time points through arc-cosine function. This metric quantifies the similarity between two decision boundaries, with the angle reflecting the degree of concept drift detection. In other words, a larger angle indicates a higher probability of detecting concept drift. ABH offers good interpretability, as its angle has a geometric presentation, and it is time-efficient, requiring only the calculation of an arc-cosine function. To validate the effectiveness of the ABH, we integrate it into the Drift Detection Model (DDM) framework, replacing error rate-based metrics to monitor data distribution over time. Empirical studies on synthetic datasets show that ABH achieves approximately a 50% reduction in the Coefficient of Variation (Cv) compared to error rate-based approaches, demonstrating the stability of ABH. On the Shanghai and Shenzhen Stock Exchanges, our model outperforms the recent models. For instance, our model outperforms the Weighted Increment-Decrement Support Vector Machine (WIDSVM), reducing the error rate by 4% and 1%, respectively.","url_abs":"https://doi.org/10.1007/s10489-025-06292-w","url_pdf":"https://doi.org/10.1007/s10489-025-06292-w","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-utility-of-hyperplane-angle-metric-in","repo_url":"https://github.com/MultiPaperCode/The-utility-of-hyperplane-angle-metric-in-detecting-financial-concept-drift","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"arc","task_name":"ARC"},{"task_slug":"drift-detection","task_name":"Drift Detection"},{"task_slug":"geometric-matching","task_name":"Geometric Matching"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}