将ARIMA与多项式分类器并行结合，提升时间序列预测精度。
Enhancing Time Series Forecasting via a Parallel Hybridization of ARIMA and Polynomial Classifiers
- 并行融合ARIMA的线性建模与多项式分类器的非线性拟合能力。
- 在多领域真实数据集上，预测精度显著优于单一模型。
- 适合需要高精度预测的金融、工业等场景使用。
时间序列预测受到广泛关注，催生了从传统统计方法到先进深度学习模型的多种方法。其中，自回归积分滑动平均（ARIMA）模型因其在经济、工业和社会数据中建模时序依赖关系的有效性，仍是广泛应用的线性技术。另一方面，多项式分类器能有效捕捉非线性关系，在股票价格预测等领域表现优异。本文提出一种混合预测方法，将ARIMA模型与多项式分类器结合，以发挥两者互补优势。该方法在多个跨领域的实际时间序列数据集上进行评估，基于预测准确性和计算效率进行比较。实验结果表明，所提出的混合模型在预测精度上持续优于单一模型，尽管执行时间略有增加。
原文摘要 · Abstract (English)
Time series forecasting has attracted significant attention, leading to the de-velopment of a wide range of approaches, from traditional statistical meth-ods to advanced deep learning models. Among them, the Auto-Regressive Integrated Moving Average (ARIMA) model remains a widely adopted linear technique due to its effectiveness in modeling temporal dependencies in economic, industrial, and social data. On the other hand, polynomial classifi-ers offer a robust framework for capturing non-linear relationships and have demonstrated competitive performance in domains such as stock price pre-diction. In this study, we propose a hybrid forecasting approach that inte-grates the ARIMA model with a polynomial classifier to leverage the com-plementary strengths of both models. The hybrid method is evaluated on multiple real-world time series datasets spanning diverse domains. Perfor-mance is assessed based on forecasting accuracy and computational effi-ciency. Experimental results reveal that the proposed hybrid model consist-ently outperforms the individual models in terms of prediction accuracy, al-beit with a modest increase in execution time.
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