The paper introduces a multimodal anomaly detection framework that uses cross‑modal reconstruction of heterogeneous time‑series sensor data to detect faults in industrial systems. By learning to reconstruct each modality from the others, the method leverages complementary information across sensing channels without requiring explicit temporal alignment or identical sampling rates. An adaptive test‑time thresholding mechanism further improves robustness to distribution shifts caused by changing operating conditions, as demonstrated by strong fault detection performance in three industrial case studies, especially under out‑of‑distribution regimes.
By Magnus Munk Jensen, Dorte Hammersh{\o}i, Rafa{\l} Wi\'sniewski, Olga Fink
arXiv:2510. 24043v4 Announce Type: replace Abstract: This paper presents Two-Stage LKPLO, a novel multi-stage outlier detection framework that overcomes the coexisting limitations of conventional projection-based methods: their reliance on a fixed statistical metric and their assumption of a single data structure.
By Akira Tamamori
The paper proposes a unified framework for estimating sparse precision and covariance matrices by reparametrizing the last column of either matrix. This reparametrization reduces the likelihood optimization to a one‑dimensional minimization followed by a penalized multiple regression, applicable to any penalty separable in off‑diagonal entries and linear on the diagonal. When the penalty is the α1 norm, the method simultaneously recovers the graphical Lasso and the covariance graphical Lasso algorithms.
By Aramayis Dallakyan, Mohsen Pourahmadi
arXiv:2606. 00052v1 Announce Type: new Abstract: As Industry 4.
By MD Shafikul Islam, Jordan Carden
arXiv:2412. 07041v4 Announce Type: replace-cross Abstract: Recovering incomplete multidimensional tensor-structured data is a fundamental task in many real-world applications.
By Mengying Lei, Lijun Sun
arXiv:2606. 14040v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are typically trained to reconstruct the \textbf{entire} residual stream through a sparse dictionary, implicitly assuming that all activation content is amenable to sparse, monosemantic decomposition.
By Ruixuan Deng, Zehao Jin, Zekun Wang, Zihan Dong
arXiv:2606.08560v2 Announce Type: replace-cross
Abstract: We adopt the canonical polyadic (CP) decomposition to model high-dimensional tensor time series. Our primary goal is to identify and estimate...
By Jinyuan Chang, Guanglin Huang, Qiwei Yao, Long Yu
arXiv:2604.17998v2 Announce Type: replace
Abstract: Anomaly detection in multivariate time series is a central challenge in industrial monitoring, as failures frequently arise from complex temporal d...
By Pooyan Khosravinia, Jo\~ao Gama, Bruno Veloso
arXiv:2609.14307v1 Announce Type: new
Abstract: Low-rank tensor factorization provides a flexible framework for completing multidimensional data from incomplete and corrupted observations. However, u...
By Binghao Wang, Feng Zhang, Wendong Wang, Jianjun Wang
The paper introduces a Physics-informed Predictive Prior Tensor Autoencoder (PPPTAE) for anomaly detection in multi-dimensional time series. It combines reconstruction-based and prediction-based autoencoders by embedding a Bayesian fusion approach and a predictive prior that respects tensor correlations. The method incorporates physical laws via tensor low‑rank decomposition to prevent over‑generalization and is validated on real‑world datasets.
By Jianan Liu, Chunguang Li
The paper introduces Sparse Separable Factor Analysis (SSFA), a latent factor model designed for complex-valued arrays that preserves amplitude and phase information. SSFA models each mode’s covariance with a low‑rank Hermitian factor structure plus a diagonal residual, applying element‑wise lasso penalties to achieve interpretable, phase‑preserving loadings via complex soft‑thresholding. The method is validated through simulations showing improved covariance estimation over vectorization approaches and applied to local field potential data from mice to compare separability across brain region, frequency, and time, as well as to impute missing recordings due to electrode misplacement.
By Ian Hultman, Kirtikanth Kalapatapu, Yassine Filali, Rainbo Hultman, Sanvesh Srivastava
arXiv:2606. 02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the structure of anomalies may be spread across multiple channels.
By Marc Pinet (LIG), Julien Cumin (LIG), Samuel Berlemont (LIG), Dominique Vaufreydaz (LIG)