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arXiv research

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.

168,657 papers · 148 categories

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121241362482 · Jun 202019922001200920172026
48 results for order detection

GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.

problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.

We propose a method for detection and prediction of native and synthetic iceberg orders on Chicago Mercantile Exchange. Native (managed by the exchange) icebergs are detected using discrepancies between the resting volume of an order and the actual trade size as indicated by trade summary messages, as well as by tracki…

2019-09-20abs ↗pdf ↗

Study slopes on knot manifolds to understand their fundamental groups.

problem Characterize slopes on knot manifolds to determine fundamental group properties.
method Develops new order-detection notions, parallels existing slope detection methods, and uses dynamics of 3-manifold group actions.
result Conjectured structure theorems connecting Heegaard-Floer homology and foliation dynamics to left-orderability.

Real-time detection of spoofing in cryptocurrency exchanges using neural networks.

problem Detecting and mitigating spoofing activity in limit order books.
method Novel order flow variables based on multi-scale Hawkes processes and a probabilistic market manipulation gain model.
result 31% of large orders could spoof the market, highlighting the importance of posting distance in price formation.

Method detects effects of synthesis parameters on plutonium oxide microstructure.

problem Detecting effects of synthesis parameters on material microstructure.
method Copula theory, high dimensional distribution distances, and permutational statistics.
result Effects of strike order and oxalic acid feed on plutonium oxide microstructure detected.

New method uses statistical physics to detect financial market manipulation.

problem Detecting financial market manipulation activities like spoofing and layering.
method Modeling order book dynamics as particle motion and using momentum measure.
result Method outperforms conventional Z-score-based anomaly detection.

The paper extends techniques to create non-left-orderable manifolds from links with multiple boundary components.

problem Creating infinite families of non-left-orderable manifolds from Dehn fillings.
method Order-detection of slopes to generalize techniques for multiple boundary components.
result Produces an infinite family of non-left-orderable Dehn fillings for hyperbolic links, including the Whitehead link.

Framework detects and ranks suspicious market manipulation using temporal convolutions and expert assessment.

problem Detecting and deterring rogue agents in financial markets.
method Weakly supervised learning, expert assessment, similarity search.
result Promising preliminary results in detecting and ranking suspicious market manipulation.

Paper explores limits of high-order clustering with planted structures.

problem Statistical and computational limits of high-order clustering with planted structures.
method Developed methods for detection and recovery of clusters, identified signal-to-noise ratio boundaries.
result Sharp boundaries of signal-to-noise ratio for statistical and computational feasibility.

Bayesian method detects mesoscale structures in pathway data networks.

problem Mesoscale structures in pathway data networks are hard to detect due to dependencies between interactions.
method Bayesian approach modeling optimal partitioning and higher-order dynamics.
result Method can recover both proximity-based and role-based groupings of nodes.

Study detects signal in financial stock correlations using phase-ordering kinetics.

problem Detecting meaningful signals in financial stock return correlations.
method Stochastic field theory model to establish a detection threshold.
result Detection of a signal in the largest eigenvalues of the stock return correlation matrix.

Paper proposes a new MIMO detection algorithm using Gaussian Mixture Expectation Propagation.

problem Challenges in MIMO detection due to interference and noise in high-order high-dimensional systems.
method The approach uses a Gaussian Mixture Model (GMM) approximation for Belief Propagation (BP) and Expectation Propagation (EP) messages to improve detection accuracy.
result The proposed algorithm outperforms state-of-the-art detection algorithms while maintaining low computational complexity.

FraudTransformer detects payment fraud by preserving event order and time gaps.

problem Detecting payment fraud in real-world banking streams with irregular time gaps.
method Augments a GPT-style architecture with a dedicated time encoder and a learned positional encoder.
result FraudTransformer outperforms classical and transformer baselines, achieving highest AUROC and PRAUC on held-out test set.

We show that the properties of admitting a co-oriented taut foliation and having a left-orderable fundamental group are equivalent for rational homology 33-sphere graph manifolds and relate them to the property of not being a Heegaard-Floer L-space. This is accomplished in several steps. First we show how to detect fa…

2014-01-30abs ↗pdf ↗

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.

The paper develops a method to predict the latent deterioration phase in limit order books before stress is observed.

problem Limit order books can transition rapidly from stable to stressed conditions, making it difficult to detect the latent deterioration phase.
method The paper formalizes a three-regime causal data-generating process and proposes a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding.
result The proposed method achieves mean lead-time of +18.6 timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines.

Deep semi-supervised anomaly detection improves fraud detection in financial markets.

problem Detecting fraud in high-frequency financial data with limited labeled examples.
method Evaluation of Deep Semi-Supervised Anomaly Detection (Deep SAD) on proprietary limit order book data.
result Deep SAD significantly improves fraud detection accuracy with minimal labeled data.

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%.

We address an anomaly detection setting in which training sequences are unavailable and anomalies are scored independently of temporal ordering. Current algorithms in anomaly detection are based on the classical density estimation approach of learning high-dimensional models and finding low-probability events. These al…

2016-09-28abs ↗pdf ↗

Method detects jumps in high-frequency order prices using local minima.

problem Detecting jumps in high-frequency order prices with noisy data.
method Developed methods to estimate, locate and test for jumps using local minima of best ask quotes.
result Consistently estimated jump sizes and times, established asymptotic properties of tests, and demonstrated faster convergence rates.

New method detects left-orderable surgeries on knot 6_2.

problem Detecting left-orderable fundamental groups of Dehn surgeries on knots.
method Using hyperbolic PSL~(2,R)\widetilde{PSL}(2,\mathbb{R})-representations.
result All Dehn surgeries on knot 6_2 with specified slopes have left-orderable fundamental groups.

Prove strong ribbon concordance induces a partial order on links, certify minimality for a handful of knots, and find minimal ribbon minimal knots.

problem Prove strong ribbon concordance induces a partial order on links.
method Use results from knot Floer homology to certify minimality under the ribbon partial order.
result Certify minimality for a handful of knots and find minimal ribbon minimal knots.

Paper tackles LL-space conjecture for knot manifolds, proving equivalence for some properties.

problem Tackles LL-space conjecture for knot manifolds, proving equivalence for some properties.
method Introduces relative LL-space conjecture, characterizes slope detection, uses Heegaard Floer homology, left-orders, and foliations.
result Confirms equivalence of CTFCTF and NLSNLS for slope detected knots, identifies exceptional slopes.

New method combines hypergraph structure and node attributes for better community detection.

problem Improving community detection in hypergraphs with node attributes.
method Developed a principled model that learns from data to combine higher-order interactions and node attributes.
result Strong performance in hyperedge prediction and community detection, especially when attributes are informative.

Optimizes variational autoencoder for detecting missing data in Mars rover transmissions.

problem Detecting missing data in Mars rover transmissions to prevent volume loss and corruption.
method Applies derivative-free optimization to tune variational autoencoder.
result Improves variational autoencoder's ability to detect missing data, aiding GDSA team.

New conic quadratic formulations improve outlier detection in regression models.

problem Detecting outliers in regression models with corrupted data.
method Deriving stronger second-order conic relaxations without big-M constraints.
result Proposed formulations are significantly faster than existing methods.

Paper proposes BOCPD for real-time order flow and market impact prediction.

problem Persistent order flow patterns in financial markets.
method Bayesian online change-point detection (BOCPD) with score-driven approach.
result Model outperforms existing models in predicting order flow and market impact.

Paper studies signal detection in noisy environments with limited communication.

problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.

Generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. In this paper, we investigate GAN to perform anomaly detection on time series dataset. In order to achieve this goal, a bibliography is made focusing on theoretical properties of GAN and …

2018-12-06abs ↗pdf ↗

Optimizes latency and false alarm probability in change detection problems.

problem Balancing latency and false alarms in non-stationary environments.
method Develops order-optimal change detectors under specified latency and false alarm levels.
result Derives a universal lower bound on latency and develops order-optimal detectors.

It is known that knot Floer homology detects the genus and Alexander polynomial of a knot. We investigate whether knot Floer homology of KK detects more structure of minimal genus Seifert surfaces for KK. We define an invariant of algebraically slice, genus one knots and provide examples to show that knot Floer homol…

2009-01-14abs ↗pdf ↗

With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algo…

2019-07-04abs ↗pdf ↗

Master algorithm fails to detect non-stationarity in practical settings.

problem Non-Stationary Reinforcement Learning without prior knowledge.
method Master algorithm tested under various conditions, including piecewise stationary multi-armed bandits.
result Master's non-stationarity detection is ineffective for practical horizons, leading to performance similar to random restarting.