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.
Unified pipeline predicts equipment anomalies with high precision and reduced false positives.
The behaviors of patients with depression are usually difficult to predict because the patients demonstrate the symptoms of a depressive episode without a warning at unexpected times. The goal of this research is to build algorithms that detect signals of such unusual moments so that doctors can be proactive in approac…
Automated anomaly detection is essential for managing information and communications technology (ICT) systems to maintain reliable services with minimum burden on operators. For detecting varying and continually emerging anomalies as differences from normal states, learning normal relationships inherent among cross-dom…
ACA identifies and explains anomalies in data.
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…
Deep learning autoencoder detects bee colony anomalies.
Topological anomaly scores predict return curves in S&P 500 stocks
We develop a supervised machine learning model that detects anomalies in systems in real time. Our model processes unbounded streams of data into time series which then form the basis of a low-latency anomaly detection model. Moreover, we extend our preliminary goal of just anomaly detection to simultaneous anomaly pre…
Deep RL predicts equipment maintenance from sensor data.
Dividing deep learning models for consistent anomaly detection in changing log data.
Mobile networks possess information about the users as well as the network. Such information is useful for making the network end-to-end visible and intelligent. Big data analytics can efficiently analyze user and network information, unearth meaningful insights with the help of machine learning tools. Utilizing big da…
Data depth aids in identifying anomalies in multivariate data.
ECAD detects anomalies without data exchangeability, improving traffic flow detection.
Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For insta…
Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal predicti…
Study detects anomalies in robot vision data to predict hazards.
Anomaly-aware forecast improves accuracy for extreme events.
Status prediction and anomaly detection are two fundamental tasks in automatic IT systems monitoring. In this paper, a joint model Predictor & Anomaly Detector (PAD) is proposed to address these two issues under one framework. In our design, the variational auto-encoder (VAE) and long short-term memory (LSTM) are joine…
Research predicts XRP price anomalies using graph topologies.
AER combines auto-encoder and LSTM for better time series anomaly detection.
Bayesian autoencoders quantify anomaly uncertainty for safer machine learning.
New method detects anomalies in computing centers' logs.
CoCAI uses copulas for accurate multivariate time-series forecasting and anomaly detection.
A modular object in a symmetric monoidal bicategory is a Frobenius algebra object whose product and coproduct are biadjoint, equipped with a braided structure and a compatible twist, satisfying rigidity, ribbon, pivotality, and modularity conditions. We prove that the oriented 3-dimensional bordism bicategory of 1-, 2-…
STRIC detects anomalies in time series by analyzing residual signals.
In recent years, there have been many practical applications of anomaly detection such as in predictive maintenance, detection of credit fraud, network intrusion, and system failure. The goal of anomaly detection is to identify in the test data anomalous behaviors that are either rare or unseen in the training data. Th…
Proposes BA method for unbiased time series anomaly detection evaluation.
PPC detects anomalies in high-dimensional data efficiently.
Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an …
This work builds a sensor graph from DC sensors for anomaly detection.
Method explains anomaly detection by generating normal modifications.
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…
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
C-PP-COAD detects anomalies with limited real data, reducing dependency on real calibration data.
Sensor data analysis plays a key role in health assessment of critical equipment. Such data are multivariate and exhibit nonlinear relationships. This paper describes how one can exploit nonlinear dimension reduction techniques, such as the t-distributed stochastic neighbor embedding (t-SNE) and kernel principal compon…
Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noi…
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator c…
Ray Singer torsion is a numerical invariant associated with a compact Riemannian manifold equipped with a flat bundle and a Hermitian structure on this bundle. In this note we show how one can remove the dependence on the Riemannian metric and on the Hermitian structure with the help of a base point and of an Euler str…
In this paper we address three main objections of behavioral finance to the theory of rational finance, considered as anomalies the theory of rational finance cannot explain: Predictability of asset returns, The Equity Premium, (The Volatility Puzzle. We offer resolutions of those objections within the rational finance…
New theory captures framing anomaly in gauge theory.
Develops probabilistic forecasting for Sea Level Anomalies using Conformal Prediction on functional time series.
Deep neural networks predict CVCM track circuit failures early.
New methods improve anomaly detection with reduced false positives.
ReGEN-TAD detects anomalies in financial time series with interpretable models.
CCE improves anomaly detection metrics by measuring both confidence and consistency.
Machine-learned anomaly detection in new-physics searches needs calibration and look-elsewhere correction
TimeInf estimates data contribution in time series data, improving model performance and anomaly detection.
Bayesian method estimates contamination factor for unsupervised anomaly detection.