Study confirms USD/JPY rises at Gotobi days, suggesting trading strategy.
problem Verifying trading strategy based on Gotobi anomaly.
method Analyzing USD/JPY rate trends and examining arbitrage opportunities.
result Valid trading strategy identified for Gotobi anomaly.
Method detects anomalies in small, imbalanced data sets.
problem Anomaly detection in small, imbalanced data sets.
method A novel (1+ε)-class classification method. result Better performance on anomaly detection problems.
Study compares statistical and machine learning models for detecting crypto trading anomalies.
problem Detecting outliers in cryptocurrency limit order books for market dynamics analysis.
method Comprehensive comparative analysis of 13 diverse models using a unified testing environment.
result Empirical Covariance (EC) model outperforms standard Buy-and-Hold by 6.70%.
Insider trading is one of the numerous white collar crimes that can contribute to the instability of the economy. Traditionally, the detection of illegal insider trades has been a human-driven process. In this paper, we collect the insider tradings made available by the US Securities and Exchange Commissions (SEC) thro…
Method detects insider trading using trading data and dimensionality reduction.
problem Identifying insider trading in large datasets.
method Unsupervised machine learning, principal component analysis, autoencoders.
result Identifies suspicious trading behavior based on reconstruction errors.
Method reveals multi-timescale trading dynamics in online financial markets.
problem Capturing and characterizing trading dynamics at different time scales.
method Non-negative tensor factorization (NTF) for multi-timescale activity patterns.
result NTF uncovers hidden activity patterns and crisis modalities in trading.
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
An Ensemble Anomaly Detection Framework for Risk Calculation Integrity
problem Detecting errors in risk valuation outputs
method Ensemble Quality Assessment Framework (EQAF)
result Achieves F1 scores of 61-79% across four datasets
Study detects unusual trading patterns on crypto exchanges using complexity measures.
problem Detecting artificial trading activity on cryptocurrency exchanges.
method Complexity and statistical-structure measures derived from high-frequency trade-level data.
result Unusual trading patterns detected on Bitget for BTC and ETH after mid-May 2025.
Fastest video anomaly detection via teacher-student distillation.
problem Anomaly detection in video at high speed.
method Adversarial knowledge distillation from object-level teacher models.
result 7-62 times faster than state-of-the-art methods.
Study finds anomalies in high-frequency S&P 500 price changes.
problem Anomalies in high-frequency S&P 500 price changes.
method Using NBBO event-time data, the study forms pairs of backward and forward price increments, standardizes them, and estimates expected responses on a fine grid of push magnitudes.
result Persistent structural shift in expected responses: near zero for short lags, pronounced tails for long lags, indicating correlation between larger historical pushes and nonzero responses.
Analyzes anomaly detection techniques for real-time streaming data.
problem Challenges in selecting effective anomaly detection techniques for real-time streaming data.
method In-depth analysis of anomaly detection techniques for real-time streaming data.
result First characterization of anomaly detection techniques across diverse fields using production data sets.
Simplifies RCA for anomalies with separable likelihoods.
problem Challenges in accurate and friendly root cause analysis.
method Bayesian framework with separable likelihoods under certain restrictions.
result Framework successfully applied to web server error logs.
A new framework uses information theory to detect anomalies in images without labeled data.
problem Detect anomalies in images without labeled data.
method A direct objective function using information theory to maximize the distance between normal and anomalous data.
result The proposed framework significantly outperforms state-of-the-arts on multiple benchmark datasets.
A quantitative check of weak efficiency in US dollar/German mark exchange rates is developed using high frequency data. We show the existence of long term return anomalies. We introduce a technique to measure the available information and show it can be profitable following a particular trading rule.
Paper presents a deep learning framework for classifying respiratory anomalies and lung diseases from sound recordings.
problem Classifying respiratory anomalies and lung diseases from respiratory sound recordings.
method The framework uses front-end feature extraction to transform sound into spectrograms, and a deep learning network to classify these features.
result The proposed deep learning system outperforms current state-of-the-art methods on the ICBHI benchmark dataset.
A new method for anomaly detection adapts to local non-stationarity in low-data regimes.
problem Adapting conformal anomaly detection to handle distribution shifts in real-world data.
method Proposes a continuous inference relaxation using continuous weighted kernel density estimation to decouple local adaptation from tail resolution.
result Restores detection capabilities and statistical power in low-data regimes while maintaining valid error control.
Model detects market anomalies using a Hawkes process with hidden Markov chain.
problem Detecting high-frequency market manipulation in cryptocurrency trades.
method Developed a Markov-modulated Hawkes process with piecewise constant excitation kernels.
result Demonstrated the model's effectiveness in detecting suspicious trading activities.
Framework detects covert financial market manipulation using LOB representations.
problem Detecting covert financial market manipulation (spoofing) from complex anomaly patterns in multilevel prices.
method Cascaded contrastive representation learning of LOB data.
result Transformer-based architectures achieve state-of-the-art results in detection performance.
We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on analytical transaction volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intens…
Deep learning model for AIS data improves vessel monitoring.
problem Improving maritime safety and efficiency with AIS data.
method Combining RNNs and latent variable modeling for multi-task learning.
result Demonstrated relevance on real AIS datasets for trajectory reconstruction, anomaly detection, and vessel type identification.
New clustering method uses Wasserstein distance to analyze simulation outputs.
problem Analyzing stochastic simulation outputs to uncover relationships and patterns.
method Agglomerative clustering using regularized Wasserstein distance.
result Identifies staffing plans yielding similar performance outcomes.
Improves anomaly detection with contaminated unlabeled data.
problem Weakness in existing semi-supervised anomaly detection methods when unlabeled data contain anomalies.
method Integrates positive-unlabeled learning with deep anomaly detection models.
result Achieves better detection performance on various datasets.
Study analyzes EU ETS carbon market dynamics, revealing inefficiencies and anomalies.
problem Inefficiencies and anomalies in EU ETS trading and pricing mechanisms.
method Empirical analysis using AR-GARCH model and weighted network analysis.
result Heterogeneous and sometimes counter-intuitive elasticities in price-volume relationships.
End-to-end anomaly detection framework using labeled anomalies.
problem Limited deep learning for anomaly detection and inefficiency of existing methods.
method Deviation learning neural network with labeled anomalies and prior probability.
result Significantly better anomaly scoring than state-of-the-art methods.
PReNet detects seen and unseen anomalies using pairwise relations.
problem Detecting unseen anomalies in semi-supervised learning.
method Pairwise Relation prediction Network (PReNet) learns anomaly and normal patterns.
result PReNet significantly outperforms nine competing methods in anomaly detection.
CANARI detects near-anomalies to predict future anomalies proactively.
problem Uncertainty in anomaly detection near distribution boundaries.
method Christoffel-based ANomaly Anticipation for eaRly dIscovery (CANARI) method.
result CANARI outperforms baseline methods in detecting near-anomalies and predicting future anomalies.
Proposes a method for anomaly detection with inexact labels.
problem Handling anomaly detection with inexact labels.
method Trains an anomaly score function using a neural network-based unsupervised method, maximizing the inexact AUC.
result Improves anomaly detection performance with inexact labels and outperforms existing methods.
Deep RL detects anomalies from few labeled examples and large unlabeled data.
problem Anomaly detection with limited labeled data and large unlabeled data.
method Deep reinforcement learning to optimize detection of labeled and unlabeled anomalies.
result Significantly outperforms state-of-the-art methods on 48 real-world datasets.
A new method assigns anomaly scores to features for better interpretation.
problem Interpreting anomaly scores from feature attributions.
method Proposes a characteristic function to attribute anomaly scores using Shapley value.
result Demonstrates the potential utility of the proposed attribution methods.
Efficient method detects point and collective anomalies in data sequences.
problem Efficiently identifying anomalies in data sequences, especially collective anomalies.
method CAPA: a computationally efficient approach for detecting collective and point anomalies.
result CAPA is consistent at detecting collective anomalies and has close to linear computational cost.
Ensemble learning improves anomaly detection for milder symptoms.
problem Difficulty in detecting incipient anomalies due to similarity to normal conditions.
method Utilize uncertainty information from ensemble learning to identify misclassified incipient anomalies.
result Ensemble learning methods show improved performance on incipient anomaly detection.
New anomaly estimator reduces bias in MLE for normally distributed data.
problem Bias in Maximum Likelihood Estimation of structured anomalies.
method Derive a new anomaly estimator using a mixture model.
result New estimator is asymptotically unbiased regardless of anomaly family size.
TPA-AD detects axle-box bearing anomalies using pseudo anomalies near normal boundaries.
problem Detecting axle-box bearing anomalies with only normal training data.
method Two-stage approach: pseudo anomalies, contrastive learning, KNN.
result Improves anomaly detection separability and sensitivity to degradation.
Paper proposes RAN for better anomaly detection in time series data.
problem Anomaly detection algorithms often fail to accurately detect anomalies due to incomplete reconstruction of anomaly data.
method RAN uses adversarial learning and latent vector-constrained Autoencoder to ensure consistent reconstruction of anomaly data.
result RAN outperforms other algorithms in detecting meaningful anomalies with higher AUC-ROC scores.
Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time tra…
A new method combines generative and feature-based approaches for unsupervised anomaly detection.
problem Identifying subtle anomalies in test samples compared to a normative distribution.
method A generative cold-diffusion pipeline trained to restore synthetically-corrupted images, combined with a novel synthetic anomaly generation procedure and ensembling restorations.
result Surpasses prior state-of-the-art for unsupervised anomaly detection in three Brain MRI datasets.
Paper tackles anomaly detection and RCA in dynamical systems using ICODE Networks.
problem Anomalies in dynamical systems impact performance and reliability.
method Proposes ICODE Networks for anomaly detection, RCA, and type classification.
result Demonstrates the ability to accurately detect anomalies, classify types, and pinpoint origins.
Study identifies high-density anomalies in normal data regions.
problem Detecting anomalies in normal data regions.
method Introduces non-parametric algorithmic frameworks for unsupervised detection.
result IPP framework yields the best detection results.
Develops efficient method to detect multiple collective anomalies in multivariate data streams.
problem Detecting anomalies in multivariate data streams, especially collective anomalies.
method MVCAPA: A method that efficiently detects multiple collective anomalies without approximations.
result MVCAPA consistently estimates the number and location of collective anomalies.
Algorithm detects anomalies in large, complex data sets.
problem Detecting shifts in data distribution, especially in high-dimensional, non-uniform data.
method Adversarial autoencoder for gaussianization of data.
result Effective detection of soft anomalies in various domains.
Improves relevancy of black-box anomaly detectors with user feedback.
problem Users often ignore many detected anomalies, requiring a method to identify and prioritize relevant ones.
method Uses user feedback to adjust anomaly selection process based on identified anomaly types.
result Significant improvements in precision and recall over various anomaly detectors.
Paper studies dual Anomaly flow under T-duality.
problem Understanding dual Anomaly flow under T-duality.
method Introduced a family of monotone functionals to estimate the dilaton function.
result Detailed examples and reductions of the dual Anomaly flow.
Paper introduces an unsupervised tensor-based anomaly detection method for spatiotemporal data.
problem Challenges in detecting anomalies in spatiotemporal data, especially in urban traffic monitoring and medical imaging.
method Formulates anomaly detection as a regularized robust low-rank + sparse tensor decomposition, incorporating spatiotemporal smoothness and local dependencies.
result Demonstrates improved anomaly detection performance on both synthetic and real data.
ABC uses autoencoders for better anomaly detection.
problem Detecting both known and unknown anomalies accurately.
method Probabilistic binary classifier using Autoencoder for normal data reconstruction.
result ABC outperforms existing methods in anomaly detection.
FAMDAD detects anomalies in mixed data using kurtosis-weighted Factor Analysis.
problem Detecting anomalies in high-dimensional mixed data.
method kurtosis-weighted Factor Analysis of Mixed Data (FAMDAD).
result Anomalies are highly separable in the first and last few dimensions of the FAMDAD embedding.
Anomaly Awareness detects anomalies in particle physics and computer vision.
problem Detect anomalies in complex data sets.
method Modifies cost function to learn normal events and anomalies.
result Effective at identifying new anomalies not previously seen.
A novel unsupervised method detects anomalies in time series data robust to warping.
problem Detecting anomalies in time series data is challenging due to warping and lack of labeled data.
method WaRTEm-AD method operates in two stages: representation learning through autoencoders and anomaly detection on learned representations.
result WaRTEm-AD effectively detects both point and sequence anomalies in time series data.