A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly. We propose a novel concept for anomaly dete…
The detection of fraud in accounting data is a long-standing challenge in financial statement audits. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known frau…
Method detects anomalies on attributed graphs with few labeled instances.
RESHAPE explains financial statement anomalies by aggregating explanations from AENNs.
Anomaly detection identifies unusual malaria transmission patterns in Ghana.
Enhances deep learning models for anomaly detection in time series data.
Paper proposes a new anomaly detection method using Random Forest with Mallows-like criterion.
A new model classifies surface anomalies in 3D point cloud data.
CAD-DA controls anomaly detection under domain adaptation.
Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks. …
MSTREAM detects anomalies in multi-aspect data streams.
Big data transforms accounting and auditing, enhancing insights but posing challenges.
New method finds local anomalies in time series by considering context information.
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
Representing maritime traffic patterns and detecting anomalies from them are key to vessel monitoring and maritime situational awareness. We propose a novel approach -- referred to as GeoTrackNet -- for maritime anomaly detection from AIS data streams. Our model exploits state-of-the-art neural network schemes to learn…
This paper evaluates anomaly detection methods for multivariate time series data.
RAID algorithm detects anomalies in real-time IoT systems.
Method identifies root causes of anomalies in causal processes.
We model anomaly and change in data by embedding the data in an ultrametric space. Taking our initial data as cross-tabulation counts (or other input data formats), Correspondence Analysis allows us to endow the information space with a Euclidean metric. We then model anomaly or change by an induced ultrametric. The in…
Intrusion detection for computer network systems has been becoming one of the most critical tasks for network administrators today. It has an important role for organizations, governments and our society due to the valuable resources hosted on computer networks. Traditional misuse detection strategies are unable to det…
In this paper we propose a novel observer-based method to improve the safety and security of connected and automated vehicle (CAV) transportation. The proposed method combines model-based signal filtering and anomaly detection methods. Specifically, we use adaptive extended Kalman filter (AEKF) to smooth sensor reading…
Unsupervised anomaly detection aids doctors in evaluating X-ray images of hands.
DiFF-RF detects point-wise and collective anomalies using random partitioning trees.
We extend the complex-valued analytic torsion, introduced by Burghelea and Haller on closed manifolds, to compact Riemannian bordisms. We do so by considering a flat complex vector bundle over a compact Riemannian manifold, endowed with a fiberwise nondegenerate symmetric bilinear form. The Riemmanian metric and the bi…
A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the const…
This work builds a sensor graph from DC sensors for anomaly detection.
The paper predicts and explains the decay of stock anomaly performance over time.
Two new scoring methods improve anomaly detection in Isolation Forest.
We argue that the AdS/CFT calculational prescription for double-trace deformations leads to a holographic derivation of the conformal anomaly, and its conformal primitive, associated to the whole family of conformally covariant powers of the Laplacian (GJMS operators) at the conformal boundary. The bulk side involves a…
We present a method for scalable and fully 3D magnetic field simultaneous localisation and mapping (SLAM) using local anomalies in the magnetic field as a source of position information. These anomalies are due to the presence of ferromagnetic material in the structure of buildings and in objects such as furniture. We …
A new approach to VAEs tackles variance shrinkage using quantile regression.
We find that when measured in terms of dollar-turnover, and once -neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of , the "Cold-Minus-Hot" (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to marke…
Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generat…
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art techniqu…
Improves anomaly detection with contaminated unlabeled data.
Nowadays, organizations collect vast quantities of accounting relevant transactions, referred to as 'journal entries', in 'Enterprise Resource Planning' (ERP) systems. The aggregation of those entries ultimately defines an organization's financial statement. To detect potential misstatements and fraud, international au…
We compute persistent homology using an intrinsic metric derived from density.
Proposes a new k-NN algorithm to improve classification accuracy by removing noise and pseudo-neighbours.
Deep RL detects anomalies from few labeled examples and large unlabeled data.
We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels. To measur…
Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples on…
A new method assigns anomaly scores to features for better interpretation.
Although deep learning has been applied to successfully address many data mining problems, relatively limited work has been done on deep learning for anomaly detection. Existing deep anomaly detection methods, which focus on learning new feature representations to enable downstream anomaly detection methods, perform in…
Ensemble learning improves anomaly detection for milder symptoms.
New anomaly estimator reduces bias in MLE for normally distributed data.