arXiv:2607. 21559v1 Announce Type: new Abstract: A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns.
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arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
RIFT (Relative Isolation From Trees) is a deterministic anomaly detection method that builds a minimum spanning tree and scores points by the sum of apparent edge sizes from each point. For one-dimensional data, RIFT reproduces the closed-form limit of Isolation Forest exactly, while in higher dimensions it offers a parameter‑free, robust alternative that avoids axis‑parallel artifacts and handles varying density and clustered anomalies. An ensemble variant is proposed for large datasets, and experiments on synthetic data and the ADBench benchmark show comparable accuracy to Isolation Forest with reduced variance across random seeds.
The paper introduces ESIA, an Earth Surface Immune System that detects and recognizes unknown anomalies in satellite imagery without prior category knowledge. It uses a non‑specific innate stage for rapid localization and a specific adaptive stage that matches image patches to text prompts via a multi‑modal model, achieving high F1 scores. The system adapts to new scenes in seconds and has been validated on a large global dataset, with applications to farmland degradation after the Kakhovka Dam collapse and burn severity assessment from the 2025 Palisades Fire.
The paper surveys recent advances in time series anomaly detection (TSAD), highlighting new architectures such as diffusion models, state space models, and large language models. It introduces a refined anomaly taxonomy, evaluates existing metrics through extensive experiments, and releases an open-source benchmark with algorithms, datasets, and evaluation tools. The authors also identify open research challenges, including dynamic thresholding, interpretability, zero‑shot transfer, multi‑modal alignment, and foundational models for time series.
arXiv:2606. 18898v1 Announce Type: new Abstract: Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare.
MORA is a drift‑robust time‑series anomaly detection framework that addresses the ambiguity between distribution drift and true anomalies by defining a temporal change disambiguation problem. It reconstructs a local target from paired short‑ and long‑term views, using the reconstruction gap as a measure of contextual support to adjust the primary anomaly score. The method requires no drift annotations or online adaptation and demonstrates strong robustness to non‑stationarity while maintaining sensitivity to genuine anomalies on four TSAD benchmarks.