On-device research index

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.

168,742 papers · 148 categories

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48 results for “local movement”

Copula-based local dependence between energy, agriculture and metal commodity marketsq-fin.CP

This study examines local co-movements in energy, agriculture, and metal markets using copulas.

problem Identifying local dependencies and asymmetries in energy, agriculture, and metal markets.

method Non-parametric mixture copula and copula-based local Kendall's tau approach.

result Increased co-movements in extreme situations, asymmetric local dependence, and diversification potential.

copulas local dependence energy markets agriculture metal commodities

2020-03-09abs ↗pdf ↗

Bayesian Inverse Reinforcement Learning for Collective Animal Movementcs.LG

Bayesian method infers local rules for collective animal movement.

problem Learn local rules governing long-term group behaviors.

method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.

result Recover true costs and find value of collective movement.

Bayesian Inverse Reinforcement Learning Collective Animal Movement Linearly-Solvable Markov Decision Process Agent-based Models Markov Decision Process

2020-09-08abs ↗pdf ↗

Optimization of Financial Instrument Parcels in Stochastic Wavelet Modelq-fin.ST

To define oscillatory movements of securities market, we put in the non-local extension of Ito- equation for wavelet-images of random processes. It is proposed an algorithm of creation of evolutionary equation and a model of prediction of the most probable price movement path. It is carried out experimental validation …

2010-07-30abs ↗pdf ↗

Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labelingcs.LG

LARA forecasts financial asset trends by refining noisy labels and extracting profitable samples.

problem Low signal-to-noise ratio and stochastic nature of financial data lead to poor predictions.

method LARA combines LA-Attention and RA-Labeling to refine and extract profitable samples.

result LARA significantly outperforms existing methods on Qlib platform.

price movement forecasting machine learning financial data signal-to-noise ratio locality-aware attention

2021-07-26abs ↗pdf ↗

Learning from Local Walks on Dynamic Graphs with Bandit Feedbackcs.LG

Study local exploration on dynamic graphs with time-varying edges.

problem Learning optimal actions in a network with changing connections.

method Local explore-then-commit algorithms under a structural condition ensuring intrinsic walk stability.

result Sublinear expected regret for reward-aware strategies.

stochastic bandits dynamic graphs local movement time-varying edges intrinsic walk stability

2026-07-12abs ↗pdf ↗

Airflow recovery from thoracic and abdominal movements using Synchrosqueezing Transform and Locally Stationary Gaussian Process Regressionstat.ML

The study recovers airflow from thoracic and abdominal movements using advanced signal processing.

problem Challenges in measuring airflow from thoracic and abdominal movements using small, inexpensive devices.

method Synchrosqueezing transform and locally stationary Gaussian process regression.

result Accurate prediction of airflow achieved in both normal sleep and anesthesia transition cases.

synchrosqueezing transform locally stationary Gaussian process respiratory signals ambulatory monitoring general anesthesia

2020-08-11abs ↗pdf ↗

Entropy-Assisted Quality Pattern Identification in Financeq-fin.TR

The paper proposes a method to identify high-quality financial patterns using entropy.

problem Extracting reliable short-term patterns from noisy financial data.

method Entropy-assisted framework for clustering and pruning patterns.

result High-quality patterns with low local entropy and historical profitability.

financial time series algorithmic trading entropy pattern identification short-term price movements

2025-03-08abs ↗pdf ↗

Framework for Inferring Following Strategies from Time Series of Movement Datastat.ML

How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize "Coordination Strategy Inference Problem". In this setting, a group of multiple individ…

2019-11-04abs ↗pdf ↗

A Probabilistic Framework for Imitating Human Race Driver Behaviorcs.RO

ProMoD models human race drivers with probabilistic movement primitives and neural networks.

problem Challenging task of modeling human driver behavior due to variability and complexity.

method Modular framework with Probabilistic Movement Primitives, clothoids, and neural networks.

result Significant advantages in imitation accuracy and robustness compared to other algorithms.

driver behavior imitation learning probabilistic modeling race car modular architecture

2020-01-22abs ↗pdf ↗

Queue Imbalance as a One-Tick-Ahead Price Predictor in a Limit Order Bookq-fin.TR

We investigate whether the bid/ask queue imbalance in a limit order book (LOB) provides significant predictive power for the direction of the next mid-price movement. We consider this question both in the context of a simple binary classifier, which seeks to predict the direction of the next mid-price movement, and a p…

2015-12-11abs ↗pdf ↗

COVID19-HPSMP: COVID-19 Adopted Hybrid and Parallel Deep Information Fusion Framework for Stock Price Movement Predictionq-fin.ST

AI model predicts stock prices using social media data and hybrid neural networks.

problem Predicting stock price movements during the COVID-19 pandemic.

method Integrates social media trends and historical stock data using a hybrid CNN-BLSTM framework.

result The proposed framework outperforms traditional models in predicting stock price movements.

AI Machine Learning Deep Learning Stock Prediction Social Media

2021-01-02abs ↗pdf ↗

Dimensionality Reduction of Movement Primitives in Parameter Spacecs.RO

Paper reduces movement primitive dimensionality in parameter space.

problem High dimensionality of movement primitives makes policy optimization expensive.

method Investigates dimensionality reduction in parameter space, identifying principal movements.

result Dimensionality reduction in parameter space is more effective than in configuration space.

dimensionality reduction movement primitives parameter space reinforcement learning robotics

2020-02-26abs ↗pdf ↗

Automatic Posture and Movement Tracking of Infants with Wearable Movement Sensorscs.CV

Infants' spontaneous and voluntary movements mirror developmental integrity of brain networks since they require coordinated activation of multiple sites in the central nervous system. Accordingly, early detection of infants with atypical motor development holds promise for recognizing those infants who are at risk for…

2019-09-21abs ↗pdf ↗

Long time existence of Minimizing Movement solutions of Calabi flowmath.DG

We recast the Calabi flow in DeGiorgi's language of minimizing movements. We establish the long time existence of minimizing movements for K-energy with arbitrary initial condition. Furthermore we establish some a priori regularity of these solutions, and that sufficiently regular minimizing movements are smooth soluti…

2012-08-13abs ↗pdf ↗

Taureau: A Stock Market Movement Inference Framework Based on Twitter Sentiment Analysiscs.CY

Taureau uses Twitter sentiment analysis to predict stock market movement.

problem Predicting stock market movement using public opinion on Twitter.

method Obtained historical tweets, filtered and labeled, generated word embeddings, assessed sentiment scores, correlated with stock price movement, designed and evaluated predictive model.

result Taureau can predict stock price movement from lagged sentiment scores.

stock market sentiment analysis Twitter public opinion predictive model

2023-03-30abs ↗pdf ↗

Evolutionary Dynamics of Investors Expectations and Market Price Movementq-fin.ST

The study models market price movement based on investors' expectations.

problem Understanding the dynamics of investors' expectations and market price movement.

method Developed a non-linear evolutionary equation linking investors' expectations and market asset price movement.

result Model predictions co-integrated with asset time series, suggesting potential for price movement forecasting.

market dynamics investors expectations price movement forecasting co-integration

2019-12-24abs ↗pdf ↗

Learning Efficient Representations of Mouse Movements to Predict User Attentioncs.HC

Neural nets predict user attention from mouse movements.

problem Predicting user attention from mouse cursor movements.

method Investigated different mouse movement representations and trained neural networks.

result Neural networks outperform handcrafted features for predicting user attention.

mouse movements user attention attention prediction neural networks mouse cursor

2020-05-30abs ↗pdf ↗

Consistency of minimizing movements with smooth mean curvature flow of droplets with prescribed contact-angle in

\mathbb R^3

math.DG

Paper proves minimizing movements match smooth droplet flow in 3D.

problem Consistency of minimizing movements with smooth mean curvature flow.

method Proved minimizing movements coincide with smooth droplet flow.

result Minimizing movements and smooth mean curvature flow are consistent in 3D.

minimizing movements mean curvature flow droplets contact-angle 3D

2024-01-12abs ↗pdf ↗

Cross-diffusion Modeling in Macroeconomicsmath.DS

This paper deals with the stability properties of a closed market, where capital and labour force are acting like a predator-prey system in population-dynamics. The spatial movement of the capital and labour force are taken into account by cross-diffusion effect. First, we are showing two possible ways for modeling thi…

2013-02-16abs ↗pdf ↗

Movement Prediction-Adjusted Naive Forecast: Is the Naive Baseline Unbeatable in Financial Time Series Forecasting?cs.CE

MPANF improves naive forecast by incorporating directional information.

problem Challenging to surpass naive forecast in financial time series.

method Combines naive forecast with movement prediction and accuracy.

result MPANF generally outperforms common benchmarks.

financial forecasting naive forecast movement prediction forecast combination time series analysis

2024-06-20abs ↗pdf ↗

Deep Eyedentification: Biometric Identification using Micro-Movements of the Eyecs.CV

We study involuntary micro-movements of the eye for biometric identification. While prior studies extract lower-frequency macro-movements from the output of video-based eye-tracking systems and engineer explicit features of these macro-movements, we develop a deep convolutional architecture that processes the raw eye-t…

2019-06-20abs ↗pdf ↗

Affine differential geometry and smoothness maximization as tools for identifying geometric movement primitivesq-bio.NC

Neuroscientific studies of drawing-like movements usually analyze neural representation of either geometric (eg. direction, shape) or temporal (eg. speed) features of trajectories rather than trajectory's representation as a whole. This work is about empirically supported mathematical ideas behind splitting and merging…

2014-09-02abs ↗pdf ↗

Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Marketsq-fin.TR

Study identifies key trades predicting market movements.

problem Predicting future market price movements.

method Optimized neural network predictor to identify influential trades.

result Trades with specific characteristics significantly impact future price predictions.

machine learning market prediction trade analysis neural networks price movements

2024-09-08abs ↗pdf ↗

Defining an intrinsic stickiness parameter of stock price returnsq-fin.ST

The study introduces a new stickiness parameter for stock prices using a non-linear model.

problem Understanding how closely individual stocks follow a stock index's price movements.

method Developed a non-linear pricing model inspired by tectonic plate movements to measure stickiness.

result Defined a stickiness parameter for stock price returns using a novel model.

stock price returns stickiness parameter non-linear model tectonic plate movements capital asset pricing model

2020-05-05abs ↗pdf ↗

Machine Learning for Data-Driven Movement Generation: a Review of the State of the Artcs.LG

The rise of non-linear and interactive media such as video games has increased the need for automatic movement animation generation. In this survey, we review and analyze different aspects of building automatic movement generation systems using machine learning techniques and motion capture data. We cover topics such a…

2019-03-20abs ↗pdf ↗

Data-Poisoning Audits for Causal Effect Estimationstat.ML

Framework detects and mitigates data-poisoning attacks in causal effect estimation.

problem Vulnerability to append-only attacks in observational causal analyses.

method Develops a data-poisoning audit for augmented inverse-probability-weighted estimation.

result Proposes a greedy scan to compute exact worst-case movement at every append budget.

data-poisoning causal effect estimation append-only attacks greedy scan total-influence score

2026-07-22abs ↗pdf ↗

Data Movement Is All You Need: A Case Study on Optimizing Transformerscs.LG

Optimizing data movement significantly improves transformer training efficiency.

problem Data movement is a major bottleneck in training transformers.

method Developed a recipe to globally optimize data movement in transformers.

result Achieved up to 1.30x performance improvement over state-of-the-art frameworks.

transformers data movement performance optimization Amdahl's Law deep neural networks

2020-06-30abs ↗pdf ↗

Forecasting Sparse Movement Speed of Urban Road Networks with Nonstationary Temporal Matrix Factorizationcs.LG

NoTMF forecasts sparse urban road movement speeds with nonstationary temporal matrix factorization.

problem Sparse and nonstationary movement speed data from urban roads.

method Nonstationary Temporal Matrix Factorization (NoTMF) model.

result NoTMF outperforms baseline models in forecasting urban road movement speeds.

Urban roads Movement speed Sparse data Nonstationary Matrix factorization

2022-03-20abs ↗pdf ↗

On certain properties of pseudo-Riemannian Bertrand manifoldsmath.DG

Study on pseudo-Riemannian Bertrand manifolds finds no closed movement systems.

problem Exploring properties of pseudo-Riemannian Bertrand manifolds.

method Proof of properties and discussion of the theory.

result No pseudo-Riemannian completely Bertrand systems exist.

pseudo-Riemannian manifolds Bertrand manifolds closed movement systems 2-dimensional manifold central field

2022-12-04abs ↗pdf ↗

Towards Multimodal MIR: Predicting individual differences from music-induced movementcs.LG

Study identifies personality traits from dance movements in music.

problem Predicting individual differences from music-induced movement.

method Identified Big Five personality traits and EQ/SQ scores from dance movements.

result Successfully explored unseen space for personality and EQ/SQ.

Music Information Retrieval Personality Traits Movement Analysis Big Five Empathy Quotient

2020-07-21abs ↗pdf ↗

Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memorycs.LG

New algorithm reduces dynamic regret in time-varying movement costs.

problem Dynamic regret in online convex optimization with time-varying movement costs.

method Introduced a novel algorithm for time-varying movement costs, achieving comparator-adaptive dynamic regret bound.

result Established first comparator-adaptive dynamic regret bound of

\widetilde{\mathcal{O}}(\sqrt{(M^2+MP_T)(T+\sum_t λ_t)})

dynamic regret online convex optimization time-varying movement costs delayed feedback time-varying memory

2026-02-06abs ↗pdf ↗

Learning Task-Agnostic Action Spaces for Movement Optimizationcs.LG

Generically learns movement control policies from exploration data.

problem Movement optimization in physically based characters.

method Parameterizes actions as target states, learns low-level control policy.

result Improves movement optimization across multiple tasks and algorithms.

movement optimization action spaces low-level control target states generality

2020-09-22abs ↗pdf ↗

Faster Perturbed Stochastic Gradient Methods for Finding Local Minimamath.OC

Paper proposes faster method to find local minima in nonconvex optimization.

problem Escaping saddle points and finding local minima in nonconvex optimization.

method LENA (Last stEp shriNkAge) framework for faster perturbed stochastic gradient methods.

result LENA finds

(ε, ε_{H})

-approximate local minima within

ilde O(ε^{-3} + ε_{H}^{-6})

evaluations.

nonconvex optimization local minima saddle points stochastic gradient perturbed methods

2021-10-25abs ↗pdf ↗

Minimizing movements for forced anisotropic curvature flow of dropletsmath.AP

Study on droplet flow on uneven surfaces, proving existence and properties.

problem Understanding droplet movement on irregular surfaces.

method Existence of smooth flow and 1/2-Hölder continuous minimizing movement solutions.

result Properties of minimizing movements including comparison principles and uniform boundedness.

droplet flow anisotropic curvature minimizing movement Young's law perimeter

2024-03-14abs ↗pdf ↗

Corporate Fundamentals and Stock Price Co-Movementq-fin.ST

Framework analyzes stock price co-movement with fundamentals using big data.

problem Understanding complex relationships between stock price co-movements and fundamental characteristics.

method Advanced big data techniques, four regression models.

result Identifies leading co-movement stocks and their influencing factors.

stock price co-movement fundamentals big data regression models Chinese banking sector

2024-11-06abs ↗pdf ↗

De Giorgi varifold solutions to Mean Curvature Flow: a minimizing movements approachmath.AP

Alternative proof of weak solutions to mean curvature flow using minimizing movements.

problem Existence of weak solutions to mean curvature flow and volume preserving mean curvature flow.

method Proposes a new existence proof using a minimizing movements scheme and a novel proxy for distance.

result Unconditional convergence towards a De Giorgi solution for the minimizing movements scheme.

mean curvature flow minimizing movements De Giorgi solutions weak solutions volume preserving

2026-07-04abs ↗pdf ↗

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratioq-fin.ST

The Autoencoder Reconstruction Ratio detects increased asset co-movements.

problem Detecting changes in asset co-movements for risk management.

method Uses a deep sparse denoising autoencoder to measure asset returns with latent variables.

result Lower ARR values indicate periods of market weakness and increased volatility.

asset co-movements risk management autoencoder latent variables real-time indicator

2020-01-23abs ↗pdf ↗

Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devicescs.CR

Wrist movements can reveal digits, posing security risks.

problem Security vulnerabilities in wrist wearable devices.

method Machine learning model trained on wrist movement data.

result 100% accuracy in predicting digits via wrist movement.

wrist wearable security digit recognition machine learning security risks

2020-04-22abs ↗pdf ↗

Deep semantic gaze embedding and scanpath comparison for expertise classification during OPT viewingcs.LG

Novel CNN-based gaze scanpath comparison distinguishes experts from novices in dental radiograph interpretation.

problem Distinguishing expertise in dental radiograph interpretation based on gaze behavior.

method Convolutional neural networks (CNN) process scene information at the fixation level, using image patches as input to compare gaze scanpaths.

result 93% accuracy in distinguishing experts from novices using image patch features.

eye movement gaze analysis expertise classification convolutional neural networks scanpath comparison

2020-03-31abs ↗pdf ↗

Deep Learning for Content-based Personalized Viewport Prediction of 360-Degree VR Videoscs.CV

Deep learning predicts VR head movements for better 360-degree videos.

problem Predicting head movements in 360-degree VR videos.

method Deep learning network using position data and video content for long-term prediction.

result 16.1% improvement in prediction accuracy compared to baseline.

VR 360-degree Personalized Deep learning Head movement

2020-03-01abs ↗pdf ↗

Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Datacs.LG

Enhances stock movement prediction using Higher Order Transformers for multimodal time-series data.

problem Predicting stock movements in financial markets with complex dynamics.

method Introduced Higher Order Transformers, extending self-attention and transformer architecture to capture complex market dynamics. Employed low-rank tensor decomposition and kernel attention to manage computational complexity. Integrated technical and fundamental analysis from historical prices and tweets.

result Demonstrated effectiveness of the method on the Stocknet dataset, improving stock movement prediction.

Higher Order Transformers Stock Movement Prediction Multimodal Time-Series Data Tensor Decomposition Kernel Attention

2024-12-13abs ↗pdf ↗

Machine learning in weekly movement predictionq-fin.CP

This paper predicts weekly stock market movements using machine learning and introduces a new benchmark.

problem Predicting stock market movements using daily data and various ML models.

method Focuses on weekly movements, introduces random traders as a benchmark, uses additional features, and adjusts training datasets.

result Trained models, especially MLP, show good performance across different trends.

stock market prediction machine learning weekly movements random traders additional features

2024-07-13abs ↗pdf ↗

Predicting Stock Price Movement as an Image Classification Problemq-fin.PR

Paper uses CNN to predict stock price movement as an image classification problem.

problem Predicting stock price movement using machine learning.

method CNN-based model for classifying stock price movement based on the first hour of trading.

result The algorithm effectively separated between stock price movement classes and outperformed other strategies.

stock price prediction image classification CNN financial markets machine learning

2023-03-02abs ↗pdf ↗

PL-NMF: Parallel Locality-Optimized Non-negative Matrix Factorizationcs.LG

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including topic modeling, recommender systems and bioinformatics. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have b…

2019-04-16abs ↗pdf ↗

Resource-Constrained Adaptive Inference for Sequential Pricingstat.ML

A new pricing controller handles resource constraints to infer target prices effectively.

problem Resource constraints prevent fixed-price inference, leading to support exclusion.

method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.

result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.

pricing resource constraints inference local non-identification realized information clock

2026-06-02abs ↗pdf ↗

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Predictionq-fin.ST

Proposes a framework to predict stock movements by integrating multi-order and internal dynamics.

problem Predicting stock movements with multi-order and internal dynamics.

method Temporal generative filters and hypergraph attentions using wavelet basis.

result Framework outperforms state-of-the-art methods in terms of profit and stability.

stock prediction multi-order dynamics internal dynamics temporal generative filters hypergraph attentions

2022-11-11abs ↗pdf ↗

Simple Stochastic Order-Book Model of Swarm Behavior in Continuous Double Auctionq-fin.TR

In this study, we present a simple stochastic order-book model for investors' swarm behaviors seen in the continuous double auction mechanism, which is employed by major global exchanges. Our study shows a characteristic called "fat tail" is seen in the data obtained from our model that incorporates the investors' swar…

2014-11-09abs ↗pdf ↗

Conditional Correlations and Principal Regression Analysis for Futuresq-fin.ST

Study shows past market trends reduce or increase correlations between futures contracts.

problem Estimating and managing risk in non-stationary futures markets.

method Applied Principal Regression Analysis (PRA) to quantify past market movements' effect on correlations.

result Past up or down 10-day trends reduce or increase instantaneous correlations, respectively.

futures markets correlations Principal Regression Analysis market movements risk management

2019-12-27abs ↗pdf ↗