用频域分析提升时间序列模型可解释性，更准更稳。
FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
- 通过分析输入信号的频率成分定位关键区域
- 频域方法在多种指标上优于传统方法，尤其抗噪声波动
- 适合需要高可靠解释的时间序列分析任务
深度神经网络在多个领域表现优异，但因其黑箱特性，可解释性不足限制了实际应用。现有解释方法对时间序列数据的分析不够充分。本文提出频域导向的解释框架FreqAtt，通过评估输入信号中的相关频率，对信号进行滤波或标记关键数据。该方法在多种统计指标下进行了广泛评估，结果表明：基于频率的归因方法，尤其是与传统归因结合使用时，在不同指标上均表现优异，显著提升了时间序列模型解释的准确性与鲁棒性。
原文摘要 · Abstract (English)
Deep neural networks are among the most successful algorithms in terms of performance and scalability across different domains. However, since these networks are black boxes, their usability is severely restricted due to a lack of interpretability. Existing interpretability methods do not address the analysis of time-series-based networks specifically enough. This paper shows that an analysis in the frequency domain can not only highlight relevant areas in the input signal better than existing methods but is also more robust to fluctuations in the signal. In this paper, FreqAtt is presented - a framework that enables post-hoc interpretation of time-series analysis. To achieve this, the relevant frequencies are evaluated, and the signal is either filtered or the relevant input data is marked. FreqAtt is evaluated using a wide range of statistical metrics to provide a broad overview of its performance. The results show that using frequency-based attribution, especially in combination with traditional attribution on top of the frequency-optimized signal, provides strong performance across different metrics.
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