Kernel-based methods such as Rocket are among the most effective default approaches for univariate time series classification (TSC), yet they do not perform equally well across all datasets. We revisit the long-standing intuition that different representations capture complementary structure and show that selectively fusing them can yield consistent improvements over Rocket on specific, systematically identifiable kinds of datasets. We introduce Fusion-3 (F3), a lightweight framework that adaptively fuses Rocket, SAX, and SFA representations. To understand when fusion helps, we cluster UCR datasets into six groups using meta-features capturing series length, spectral structure, roughness, and class imbalance, and treat these clusters as interpretable data-structure regimes. Our analysis shows that fusion typically outperforms strong baselines in regimes with structured variability or rich frequency content, while offering diminishing returns in highly irregular or outlier-heavy settings. To support these findings, we combine three complementary analyses: non-parametric paired statistics across datasets, ablation studies isolating the roles of individual representations, and attribution via SHAP to identify which dataset properties predict fusion gains. Sample-level case studies further reveal the underlying mechanism: fusion primarily improves performance by rescuing specific errors, with adaptive increases in frequency-domain weighting precisely where corrections occur. Using 5-fold cross-validation on the 113 UCR datasets, F3 yields small but consistent average improvements over Rocket, supported by frequentist and Bayesian evidence and accompanied by clearly identifiable failure cases. Our results show that selectively applied fusion provides dependable and interpretable extension to strong kernel-based methods, correcting their weaknesses precisely where the data support it.

翻译：诸如Rocket等基于核函数的方法已成为单变量时间序列分类（TSC）中最有效的默认方法之一，然而它们并非在所有数据集上表现一致。我们重新审视了一个长期存在的观点：不同的表征能够捕捉互补的结构，并证明有选择性地融合这些表征可以在特定、可系统识别的数据集类型上，相比Rocket取得持续的改进。我们提出了Fusion-3（F3），一个轻量级的框架，能够自适应地融合Rocket、SAX和SFA表征。为了理解融合何时有效，我们利用捕捉序列长度、频谱结构、粗糙度和类别不平衡的元特征，将UCR数据集聚类为六个组，并将这些聚类视为可解释的数据结构状态。我们的分析表明，在具有结构化变异性或丰富频率内容的状态下，融合通常优于强基线方法，而在高度不规则或异常值较多的场景中，其收益则逐渐减少。为了支持这些发现，我们结合了三种互补的分析方法：跨数据集的非参数配对统计、分离各表征作用的消融研究，以及通过SHAP进行归因以识别哪些数据集属性能够预测融合收益。样本级的案例研究进一步揭示了其内在机制：融合主要通过纠正特定错误来提升性能，并在发生修正的位置自适应地增加频域权重。在113个UCR数据集上使用5折交叉验证，F3相比Rocket取得了虽小但持续的平均改进，这一结果得到了频率学派和贝叶斯学派的证据支持，并伴有清晰可辨的失败案例。我们的结果表明，有选择地应用融合为强大的基于核函数的方法提供了可靠且可解释的扩展，恰好在数据支持的地方纠正了它们的弱点。