Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders

• URL: http://arxiv.org/abs/2305.16189v3
• Date: Tue, 20 Feb 2024 04:18:18 GMT
• Title: Martian time-series unraveled: A multi-scale nested approach with factorial variational autoencoders
• Authors: Ali Siahkoohi and Rudy Morel and Randall Balestriero and Erwan Allys and Gr\'egory Sainton and Taichi Kawamura and Maarten V. de Hoop
• Abstract summary: Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator. This problem is inherently ill-posed and is challenged by the variety of timescales exhibited by sources. We propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering spectra.
• Score: 13.758556734097374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of timescales exhibited by sources. Existing methods typically rely on a preselected window size that determines their operating timescale, limiting their capacity to handle multi-scale sources. To address this issue, we propose an unsupervised multi-scale clustering and source separation framework by leveraging wavelet scattering spectra that provide a low-dimensional representation of stochastic processes, capable of distinguishing between different non-Gaussian stochastic processes. Nested within this representation space, we develop a factorial Gaussian-mixture variational autoencoder that is trained to (1) probabilistically cluster sources at different timescales and (2) independently sample scattering spectra representations associated with each cluster. As the final stage, using samples from each cluster as prior information, we formulate source separation as an optimization problem in the wavelet scattering spectra representation space, aiming to separate sources in the time domain. When applied to the entire seismic dataset recorded during the NASA InSight mission on Mars, containing sources varying greatly in timescale, our multi-scale nested approach proves to be a powerful tool for disentangling such different sources, e.g., minute-long transient one-sided pulses (known as ``glitches'') and structured ambient noises resulting from atmospheric activities that typically last for tens of minutes. These results provide an opportunity to conduct further investigations into the isolated sources related to atmospheric-surface interactions, thermal relaxations, and other complex phenomena.

Related papers

• 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. We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis. Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• 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)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Variational quantization for state space models [3.9762742923544456]
  forecasting tasks using large datasets gathering thousands of heterogeneous time series is a crucial statistical problem in numerous sectors. We propose a new forecasting model that combines discrete state space hidden Markov models with recent neural network architectures and training procedures inspired by vector quantized variational autoencoders. We assess the performance of the proposed method using several datasets and show that it outperforms other state-of-the-art solutions.
  arXiv  Detail & Related papers  (2024-04-17T07:01:41Z)
• Score-based Source Separation with Applications to Digital Communication Signals [72.6570125649502]
  We propose a new method for separating superimposed sources using diffusion-based generative models. Motivated by applications in radio-frequency (RF) systems, we are interested in sources with underlying discrete nature. Our method can be viewed as a multi-source extension to the recently proposed score distillation sampling scheme.
  arXiv  Detail & Related papers  (2023-06-26T04:12:40Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• Unearthing InSights into Mars: Unsupervised Source Separation with Limited Data [2.626095252463179]
  An ill-posed set of source signals have been observed through a mixing operator. This problem requires prior knowledge, or implicitly or unsupervised methods from existing data. Thanks to wavelet scattering co-variances, we are able to separate glitches using a only few glitch-free data snippets.
  arXiv  Detail & Related papers  (2023-01-27T20:38:07Z)
• Robust Audio Anomaly Detection [10.75127981612396]
  The presented approach doesn't assume the presence of labeled anomalies in the training dataset. The temporal dynamics are modeled using recurrent layers augmented with attention mechanism. The output of the network is an outlier robust probability density function.
  arXiv  Detail & Related papers  (2022-02-03T17:19:42Z)
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities [125.62877849447729]
  We consider distributed variational inequalities (VIs) on domains with the problem data that is heterogeneous (non-IID) and distributed across many devices. We make a very general assumption on the computational network that covers the settings of fully decentralized calculations. We theoretically analyze its convergence rate in the strongly-monotone, monotone, and non-monotone settings.
  arXiv  Detail & Related papers  (2021-06-15T17:45:51Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

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.