Lopes Jessica Fernandes, Barbon Junior Sylvio, de Melo Leonimer Flávio
Department of Electrical Engineering, Londrina State University (UEL), Londrina 86057-970, Brazil.
Department of Engineering and Architecture, Università degli Studi di Trieste (UNITS), 34127 Trieste, Italy.
Sensors (Basel). 2025 Apr 28;25(9):2787. doi: 10.3390/s25092787.
With the increasing demand for time-series analysis, driven by the proliferation of IoT devices and real-time data-driven systems, detecting change points in time series has become critical for accurate short-term prediction. The variability in patterns necessitates frequent analysis to sustain high performance by acquiring the hyperparameter. The Cumulative Sum (CUSUM) method, based on calculating the cumulative values within a time series, is commonly used for change detection due to its early detection of small drifts, simplicity, low computational cost, and robustness to noise. However, its effectiveness heavily depends on the hyperparameter configuration, as a single setup may not be universally suitable across the entire time series. Consequently, fine-tuning is often required to achieve optimal results, yet this selection process is traditionally performed through trial and error or prior expert knowledge, which introduces subjectivity and inefficiency. To address this challenge, several strategies have been proposed to facilitate hyperparameter optimizations, as traditional methods are impractical. Meta-learning-based techniques present viable alternatives for periodic hyperparameter optimization, enabling the selection of configurations that adapt to dynamic scenarios. This work introduces a meta-modeling scheme designed to automate the recommendation of hyperparameters for the CUSUM algorithm. Benchmark datasets from the literature were used to evaluate the proposed framework. The results indicate that this framework preserves high accuracy while significantly reducing time requirements compared to Grid Search and Genetic Algorithm optimization.
随着物联网设备和实时数据驱动系统的激增，对时间序列分析的需求不断增加，检测时间序列中的变化点对于准确的短期预测变得至关重要。模式的可变性需要频繁分析，以便通过获取超参数来维持高性能。基于计算时间序列内累积值的累积和（CUSUM）方法，由于其能早期检测到小的漂移、简单、计算成本低以及对噪声具有鲁棒性，通常用于变化检测。然而，其有效性在很大程度上取决于超参数配置，因为单一设置可能并非在整个时间序列中普遍适用。因此，通常需要进行微调以获得最佳结果，但这种选择过程传统上是通过反复试验或先验专家知识来执行的，这会引入主观性和低效率。为应对这一挑战，由于传统方法不切实际，已提出了几种策略来促进超参数优化。基于元学习的技术为周期性超参数优化提供了可行的替代方案，能够选择适应动态场景的配置。这项工作引入了一种元建模方案，旨在自动为CUSUM算法推荐超参数。使用文献中的基准数据集来评估所提出的框架。结果表明，与网格搜索和遗传算法优化相比，该框架在显著减少时间需求的同时保持了高精度。
