CLOSURE introduces a two‑task framework for multivariate time‑series anomaly detection that separately evaluates recoverability and cross‑channel consistency. The recoverability task uses a diffusion reconstructor conditioned on a discrete memory of normal temporal bases, while the consistency task employs a relation‑closure operator that predicts each channel from its peers using sparse signed gates. Experiments demonstrate that combining these two scores outperforms recent reconstruction, frequency, and topology‑based detectors on public benchmarks, highlighting the importance of assessing consistency alongside recoverability.
arXiv:2611.06441v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) relies on modeling both temporal dynamics and inter-sensor dependencies to identify complex system f...
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.
arXiv:2606. 09874v1 Announce Type: new Abstract: Reconstruction-based methods are widely used for time series anomaly detection, where models are trained to reconstruct subsequences, and anomalies are identified through reconstruction errors.
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.
LEARN-TS is a method for multivariate time‑series anomaly detection that uses a frozen language model to generate two semantic representations: one that captures window‑specific observation context without exact numerical values, and another that provides a fixed, dataset‑agnostic normality prompt. These representations guide a patch‑masked reconstruction process, allowing the model to estimate normality discrepancy and produce timestamp‑level anomaly evidence. Experiments on four benchmarks show that LEARN‑TS achieves the best mean performance in 13 of 16 dataset‑metric comparisons, and ablation studies confirm the benefits of observation conditioning, joint normality alignment, and semantic references.
arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
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...
arXiv:2606. 18539v1 Announce Type: new Abstract: Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number (e.
The paper proposes a test‑time optimization technique that converts a frozen sequence reconstructor into an imputer for missing time‑series channels. By treating missing entries as optimization variables, clamping observed data, and backpropagating to minimize reconstruction error only on observed coordinates, the method avoids the need for artificial mask augmentation or full‑channel dropout during training. Experiments on five real‑world datasets show that this refinement improves mean absolute error in most dataset‑backbone combinations and can outperform mask‑aware imputers on some datasets, though it requires more inference time and depends on cross‑channel coupling.
Complete sensor-channel failures create a difficult form of time-series missingness: local interpolation is unavailable, and deploying a separately trained imputer for every failure pattern may be imp...
arXiv:2603.12916v4 Announce Type: replace-cross Abstract: Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonom...
TENET is a Time-point Encoding Network designed for unsupervised multivariate time series anomaly detection. It reframes the task as estimating current-point system-state compatibility by constructing a latent representation of the target time point from variable-level temporal context within an input window. The model uses a temporal layer to retrieve context relative to the current point, encodes time with point-centered embeddings, and adaptively mixes this context with the current representation, then evaluates the latent state with a fixed standard-normal negative log-likelihood score, achieving state-of-the-art performance across benchmark datasets.