FREQuency ATTribution: Benchmarking Frequency-based Occlusion for Time Series Data

• URL: http://arxiv.org/abs/2506.18481v1
• Date: Mon, 23 Jun 2025 10:34:44 GMT
• Title: FREQuency ATTribution: Benchmarking Frequency-based Occlusion for Time Series Data
• Authors: Dominique Mercier, Andreas Dengel, Sheraz, Ahmed,
• Abstract summary: FreqATT is a framework that enables post-hoc networks to interpret time series analysis.<n>This paper shows that an analysis in the frequency domain can not only highlight relevant areas in the input signal better than existing methods.
• Score: 5.791090268912534
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep neural networks are among the most successful algorithms in terms of performance and scalability in different domains. However, since these networks are black boxes, their usability is severely restricted due to the 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 networks to interpret time series analysis. To achieve this, the relevant different frequencies are evaluated and the signal is either filtered or the relevant input data is marked.

Related papers

• LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation [55.800319453296886]
  Time series with missing or irregularly sampled data are a persistent challenge in machine learning.<n>We introduce a different Lombiable--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data.
  arXiv  Detail & Related papers  (2025-06-20T14:48:42Z)
• FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification [56.925103708982164]
  We present a novel perspective from the frequency domain and identify three advantages for downstream classification: global, independent, and compact.<n>We propose the lightweight yet effective Frequency Refined Augmentation (FreRA) tailored for time series contrastive learning on classification tasks.<n>FreRA consistently outperforms ten leading baselines on time series classification, anomaly detection, and transfer learning tasks.
  arXiv  Detail & Related papers  (2025-05-29T07:18:28Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain Information [10.113418621891281]
  We propose a new time series anomaly detection method called F-SE-LSTM.<n>This method utilizes two sliding windows and fast Fourier transform (FFT) to construct a frequency matrix.<n>We show that F-SE-LSTM exhibits better discriminative ability than ordinary time domain and frequency domain data.
  arXiv  Detail & Related papers  (2024-12-03T14:36:24Z)
• FLEXtime: Filterbank learning to explain time series [10.706092195673257]
  State-of-the-art methods for explaining predictions from time series involve learning an instance-wise saliency mask for each time step.<n>We propose to view time series explainability as saliency maps over interpretable parts, leaning on established signal processing methodology on signal decomposition.<n>Specifically, we propose a new method called FLEXtime that uses a bank of bandpass filters to split the time series into frequency bands.
  arXiv  Detail & Related papers  (2024-11-06T15:06:42Z)
• Accelerating Inference of Networks in the Frequency Domain [8.125023712173686]
  We propose performing network inference in the frequency domain to speed up networks whose frequency parameters are sparse. In particular, we propose a frequency inference chain that is dual to the network inference in the spatial domain. The proposed approach significantly improves accuracy in the case of a high speedup ratio (over 100x)
  arXiv  Detail & Related papers  (2024-10-06T03:34:38Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• Histogram Layer Time Delay Neural Networks for Passive Sonar Classification [58.720142291102135]
  A novel method combines a time delay neural network and histogram layer to incorporate statistical contexts for improved feature learning and underwater acoustic target classification. The proposed method outperforms the baseline model, demonstrating the utility in incorporating statistical contexts for passive sonar target recognition.
  arXiv  Detail & Related papers  (2023-07-25T19:47:26Z)
• Understanding robustness and generalization of artificial neural networks through Fourier masks [8.94889125739046]
  Recent literature suggests that robust networks with good generalization properties tend to be biased towards processing low frequencies in images. We develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance.
  arXiv  Detail & Related papers  (2022-03-16T17:32:00Z)
• Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System [2.8123846032806035]
  interference classification plays an important role in protecting the authorized communication system. In this paper, the interference classification problem for the frequency hopping communication system is discussed. Considering the possibility of presence multiple interferences in the frequency hopping system, the linear and bilinear transform based composite time-frequency analysis method is adopted.
  arXiv  Detail & Related papers  (2021-07-21T14:22:40Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Frequency Gating: Improved Convolutional Neural Networks for Speech Enhancement in the Time-Frequency Domain [37.722450363816144]
  We introduce a method, which we call Frequency Gating, to compute multiplicative weights for the kernels of the CNN. Experiments with an autoencoder neural network with skip connections show that both local and frequency-wise gating outperform the baseline. A loss function based on the extended short-time objective intelligibility score (ESTOI) is introduced, which we show to outperform the standard mean squared error (MSE) loss function.
  arXiv  Detail & Related papers  (2020-11-08T22:04:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.