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[2506.18481] FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Summary
The paper presents FREQuency ATTribution, a framework for interpreting time-series data using frequency-based occlusion, enhancing the robustness and accuracy of neural network analysis.
Why It Matters
As deep learning models become increasingly prevalent, their interpretability remains a critical challenge, particularly in time-series analysis. This research addresses the gap in existing interpretability methods, providing a more effective approach that can lead to better decision-making in applications reliant on time-series data.
Key Takeaways
- FREQuency ATTribution offers a novel method for interpreting time-series data.
- Frequency-based occlusion improves robustness against signal fluctuations.
- Combining frequency-based and traditional attribution methods yields superior results.
- The framework is evaluated using a wide range of statistical metrics.
- This approach addresses the limitations of existing interpretability methods for time-series networks.
Computer Science > Machine Learning arXiv:2506.18481 (cs) [Submitted on 23 Jun 2025 (v1), last revised 24 Feb 2026 (this version, v2)] Title:FREQuency ATTribution: benchmarking frequency-based occlusion for time series data Authors:Dominique Mercier, Andreas Dengel, Sheraz Ahmed View a PDF of the paper titled FREQuency ATTribution: benchmarking frequency-based occlusion for time series data, by Dominique Mercier and 2 other authors View PDF HTML (experimental) Abstract: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 attributi...
