arXiv research

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

A new method reduces data movement in neural network training.

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

Study develops machine learning model to predict component movement during reflow in SMT.

MPANF improves naive forecast by incorporating directional information.

Wrist movements can reveal digits, posing security risks.

News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.

Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthc…

Study of Bitcoin User Network structure over 8 years.

As described in this paper, we study market-wide price co-movements around crashes by analyzing a dataset of high-frequency stock returns of the constituent issues of Nikkei 225 Index listed on the Tokyo Stock Exchange for the three years during 2007--2009. Results of day-to-day principal component analysis of the time…

Study improves stock movement prediction using multimodal data.

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

FineHand learns hand shapes for better ASL recognition.

MDGNN predicts stock prices by capturing multifaceted relations over time.

Machine learning models predict EUR/USD currency direction with 58.52% accuracy.

Paper reduces movement primitive dimensionality in parameter space.

In this paper a multi-factor generalization of Ho-Lee model is proposed. In sharp contrast to the classical Ho-Lee, this generalization allows for those movements other than parallel shifts, while it still is described by a recombining tree, and is stationary to be compatible with principal component analysis. Based on…

The preponderance of connected devices provides unprecedented opportunities for fine-grained monitoring of the public infrastructure. However while classical models expect high quality application-specific data streams, the promise of the Internet of Things (IoT) is that of an abundance of disparate and noisy datasets …

The paper presents a method to reduce arm motion complexity for prosthetics and robotics.

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…

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…

Taureau uses Twitter sentiment analysis to predict stock market movement.

It is hypothesized that price charts can be empirically decomposed into two components as random and non random. The non random component, which can be treated as approximately regular behavior of the prices (trend) in an epoch, is a geometric line. Thus, the random component fluctuates around the non random component …

In this paper, we contribute to the literature on energy market co-movement by studying its dynamics in the time-frequency domain. The novelty of our approach lies in the application of wavelet tools to commodity market data. A major part of economic time series analysis is done in the time or frequency domain separate…

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

Neural nets predict user attention from mouse movements.

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

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…

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…

Study identifies key trades predicting market movements.

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…

This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …

Bayesian method infers local rules for collective animal movement.

Optimizing data movement significantly improves transformer training efficiency.

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

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

Study identifies personality traits from dance movements in music.

We simulate a series of daily returns from intraday price movements initiated by microstructure elements. Significant evidence is found that daily returns and daily return volatility exhibit first order autocorrelation, but trading volume and daily return volatility are not correlated, while intraday volatility is. We …

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

Generically learns movement control policies from exploration data.

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

We empirically analyze the most volatile component of the electricity price time series from two North-American wholesale electricity markets. We show that these time series exhibit fluctuations which are not described by a Brownian Motion, as they show multi-scaling, high Hurst exponents and sharp price movements. We …

PCCs combine PCA and copulas for high-dimensional tail dependence modeling.

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

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

The Autoencoder Reconstruction Ratio detects increased asset co-movements.

This research predicts stock market movements using Vision-Language models.

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