Using available data from the New York stock market (NYSM) we test four different bi-parametric models to fit the correspondent volume-price distributions at each -minute lag: the Gamma distribution, the inverse Gamma distribution, the Weibull distribution and the log-normal distribution. The volume-price data, whi…
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.
168,982 papers · 148 categories
Trend · papers per month
48 results for “data evolution”
Deep learning approximates PDE evolution operators from solution data.
problem Recovering unknown time-dependent PDEs from solution data.
method Approximate evolution operator in modal space, train deep neural network.
result Deep learning method accurately approximates PDE solutions.
Captures user behaviors in evolving time series data.
problem Modeling time series data with evolving patterns.
method Defines evolution genes, learns classifiers, and estimates segment distributions.
result Achieves better prediction results and provides explanations.
Develops robust methods for infinite-dimensional stochastic processes.
problem Measuring covariations in stochastic evolution equations in infinite dimensions.
method Asymptotic theory for jump robust measurement of covariations.
result Identifies scaling limits for realized covariations.
Algorithm improves vanilla option pricing accuracy during and before COVID-19.
problem Improving vanilla option pricing accuracy during and before the pandemic.
method Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm for bi-objective optimization.
result Algorithm approximates real market vanilla option prices more accurately than Black-Scholes.
State evolution beyond first-order methods I: Rigorous predictions and finite-sample guaranteesmath.ST
New method predicts state evolution for non-first-order algorithms on nonconvex problems.
problem Analyzing nonconvex optimization problems with random data.
method Developed a state evolution for a broader class of algorithms including first-order and saddle point updates.
result Established rigorous state evolution predictions and finite-sample guarantees for non-first-order methods.
This paper gives two methods for constructing associative 3-folds in R^7, based around the fundamental idea of evolution equations, and uses these methods to construct examples of these geometric objects. The paper is a generalisation of the work by Joyce in math.DG/0008021, math.DG/0008155, math.DG/0010036 and math.DG…
AI helps forecasters understand TC convective evolution before intensification.
problem Challenges in extracting scientific insights from complex TC data.
method Combining AI prediction algorithms and classical statistical inference.
result Identifies patterns in TC convective structure leading to intensification.
The paper tackles learning energies from time-evolving critical points.
problem Learnability of energies from data of critical points.
method Formulates a variational problem and uses Gamma-convergence arguments.
result Minimal solutions from finite observations converge to the exact energy.
Which factor dominates the industry evolution? A synergy analysis based on China's ICT industryphysics.soc-ph
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
EvoNet predicts the evolution of dynamic graphs using a graph neural network and recurrent architecture.
problem Predicting the evolution of dynamic graphs is challenging and underexplored.
method EvoNet uses a graph neural network and recurrent architecture to predict the evolution of dynamic graphs.
result EvoNet effectively predicts the evolution of dynamic graphs on both artificial and real-world datasets.
A new method evolves point clouds using B-splines for smooth surfaces.
problem Evolution of smooth surfaces from discrete point clouds.
method Adaptive Lagrangian B-spline framework for geometric evolution.
result Efficient and accurate reproduction of surface evolution phenomena.
Paper tackles unpredictable feature evolution in learning.
problem Learning with unpredictable feature evolution.
method Proposes PUFE method to fill incomplete overlapping period and formulate as matrix completion problem. Uses ensemble method to incorporate old and new feature spaces.
result Theoretical and experimental validation shows PUFE method can always follow the best base models.
The calculus correspondence has been known to exist between generic pedal evolutions and generic wave front evolutions. In this paper, we first extend the known results on the calculus correspondence to evolutions with multi-parameters, and then give applications of calculus correspondence. Moreover, we discuss the pos…
The paper ensures positivity of solutions to stochastic equations with positive initial data.
problem Ensuring positivity of solutions to stochastic equations with positive initial data.
method Providing sufficient conditions on coefficients for positivity of mild solutions.
result Sufficient conditions for positivity of solutions to stochastic equations.
We propose a methodology for clustering financial time series of stocks' returns, and a graphical set-up to quantify and visualise the evolution of these clusters through time. The proposed graphical representation allows for the application of well known algorithms for solving classical combinatorial graph problems, w…
ReGENN improves time series forecasting by considering inter and intra-temporal relationships.
problem Achieving reliable predictions in real-world time series applications.
method ReGENN combines graph evolution with deep recurrent learning to model dynamic dependencies among multiple variables.
result Sound improvement of up to 64.87% over competing algorithms in time-series forecasting.
DeepStreamCE detects new classes in streaming deep neural networks.
problem Detecting new classes in deep neural networks in a streaming environment.
method Uses autoencoder and MCOD stream-based clustering for real-time concept evolution detection.
result DeepStreamCE outperforms OpenMax in identifying concept evolution.
We present a framework for describing the evolution of stochastic observables having a non-stationary distribution of values. The framework is applied to empirical volume-prices from assets traded at the New York stock exchange. Using Kullback-Leibler divergence we evaluate the best model out from four biparametric mod…
Study material evolution using groupoids to track intrinsic properties.
problem Tracking material evolution without considering the whole body.
method Construct a groupoid encoding intrinsic properties and characteristic foliations.
result Define the evolution equation for material points.
Predicting the future evolution of complex systems is one of the main challenges in complexity science. Based on a current snapshot of a network, link prediction algorithms aim to predict its future evolution. We apply here link prediction algorithms to data on the international trade between countries. This data can b…
The paper studies circular evolutes and involutes of framed curves in Euclidean space.
problem Investigating properties of framed curves and their evolutes and involutes.
method Definition and analysis of circular evolutes and involutes of framed curves, properties of normal surfaces, and their relations.
result Circular evolutes and involutes of framed curves are opposite operations under suitable assumptions, similar to fronts in the Euclidean plane.
Paper presents a machine learning method to predict cancer sub-clones.
problem Predicting the growth of sub-clones in cancer tumors.
method Data-driven machine learning approach to capture sub-clonal characteristics.
result Machine learning can predict which sub-clones are more likely to grow.
The paper studies curve evolution using the PLR equation and its solutions.
problem Investigating the evolution of space curves governed by the PLR equation.
method Examined the Lund-Regge evolution and derived its representation in the Frenet frame, aligning with the Lax system of the PLR equation. Developed a construction method for curve families via the Sym formula.
result Described the Lund-Regge evolution corresponding to Date multi-soliton solutions to the PLR equation.
We consider the evolution of a Hermitian metric on a compact complex manifold by its Chern-Ricci form. This is an evolution equation first studied by M. Gill, and coincides with the Kahler-Ricci flow if the initial metric is Kahler. We find the maximal existence time for the flow in terms of the initial data. We invest…
The paper defines evolutes and involutes for framed curves and their properties.
problem Defining evolutes and involutes for framed curves with singular points.
method Using the theory of framed curves and Bertrand type curves.
result Conditions for evolutes and involutes being inverse operations of framed curves.
New method clusters strong and weak views effectively, improving performance by up to 40%.
problem Clustering incomplete multi-view data with unbalanced incompleteness.
method View evolution scheme and weighted multi-view subspace clustering.
result Improves clustering performance by up to 40% on three metrics.
Study predicts evolution patterns for pretzel knots, revealing abrupt transitions and hidden non-linearity.
problem Predicting evolution of Khovanov polynomials for pretzel knots.
method Conjectured explicit evolution formulas, revealed abrupt transitions, and identified additional Lyapunov exponents.
result Abrupt transitions and hidden non-linearity in evolution of Khovanov polynomials for thick knots.
Study the geometry and dynamics of skew evolutes and involutes, related to bicycle kinematics.
problem Understanding the geometry and dynamics of skew evolutes and involutes.
method Investigate the skew evolute and involute maps, comparing them to bicycle kinematics.
result The skew evolute and involute maps have properties analogous to bicycle kinematics.
The evolute of a smooth curve in an m-dimensional Euclidean space is the locus of centers of its osculating spheres, and the evolute of a spatial polygon is the polygon whose consecutive vertices are the centers of the spheres through the consecutive (m+1)-tuples of vertices of the original polygon. We study the iterat…
The paper proposes a method to learn evolving multivariate distributions from sample paths.
problem Learning the temporal evolution of multivariate densities from sample data.
method Normalizing flows to construct time-dependent mappings.
result The method can approximate evolving probability density functions from observed data.
ccc-Autoevolutesmath.DG
ccc-Autoevolutes are closed curves congruent to their evolutes, constructed via symmetry.
problem Constructing closed curves congruent to their evolutes.
method Modified Frenet equation, numerical solutions, symmetry.
result Found the smallest autoevolute as a trefoil knot.
Study evolutes of curves with varying smoothness.
problem Understanding evolutes of curves with low smoothness.
method Analyzing the relationship between curve smoothness and evolute regularity.
result Evolutes have one less order of smoothness than the parent curve in generic cases.
Gradient descent converges to perfect classification in neural nets for non-separable data.
problem Classifying linearly non-separable data using neural networks.
method Analysis of gradient descent dynamics in neural networks with sufficient but not large number of neurons.
result Gradient descent converges to global minima with perfect classification in the landscape of minimization problems.
Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient whe…
Defines horocyclic evolutes, parallels, and involutes of spacelike frontals in hyperbolic 2-space.
problem None explicitly stated; focuses on definitions and relations.
method Using enveloid theorem, defines horocyclic parallel and involute as normal envelopes of horocycles.
result Investigates relations among horocyclic evolutes, parallels, and involutes.
The determination of cluster centers generally depends on the scale that we use to analyze the data to be clustered. Inappropriate scale usually leads to unreasonable cluster centers and thus unreasonable results. In this study, we first consider the similarity of elements in the data as the connectivity of nodes in an…
Differential Evolution outperforms SMAC in hyperparameter tuning.
problem Automated hyperparameter tuning for machine learning.
method Empirical study comparing Differential Evolution to SMAC.
result Differential Evolution outperforms SMAC on most datasets.
Study cosmic structures using Topological Data Analysis and Persistence Energy.
problem Investigate cosmic web evolution in CDM cosmologies.
method Apply LITE method to embed persistence diagrams into vector spaces and analyze cosmic structures.
result Discover a correlation between Persistence Energy and redshift values.
Study on focal surfaces and evolutes of framed curves in hyperbolic 3-space using Legendrian duality.
problem Investigate differential geometry properties of framed curves, including singular points.
method Use Legendrian dualities to analyze focal surfaces and evolutes of hyperbolic framed curves.
result Show the relationship among focal surfaces, evolutes, and dual surfaces of evolutes.
Aligns text representations over time for better performance in sequential tasks.
problem Language evolution causes data drift in sequential tasks.
method Sequentially aligns learned representations to combat data drift.
result Sequential alignment outperforms strong baselines on various tasks.
Abstract In this paper, definition of involute-evolute curve couple in Galilean space is given and some well-known theorems for the involute-evolute curves are obtained in 3-dimensional Galilean space.
Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Researchers are currently investigating on the use of patient reported outcome measures for the assessment of impact a…
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly i…
Study constructs solutions for evolving hypersurfaces using inverse spacetime mean curvature.
problem Evolution of hypersurfaces in spacetime.
method Weak solutions for hypersurfaces evolving along inverse spacetime mean curvature in asymptotically flat maximal initial data sets.
result Weak solution detects both future- and past-trapped apparent horizons.
New model explains volatility after extreme stock market events.
problem Understanding volatility dynamics after extreme stock market events.
method Proposed a new dynamical model using high frequency minute data.
result Volatility after extreme events follows a stretched exponential decay initially and a power law decay later.
Geometric approach to Dirac operator evolution on spacetimes.
problem Constructing the Cauchy evolution operator for Lorentzian Dirac operators.
method Realizing the operator as a sum of oscillatory integrals, relating to Feynman propagator.
result Relating Cauchy evolution operators to Feynman propagators and constructing Hadamard states.
The paper studies spacelike curves and timelike ruled surfaces in Minkowski space.
problem Analyzing the evolution of spacelike curves and timelike ruled surfaces in Minkowski space.
method Deriving time evolution equations for curvature and torsion of spacelike curves, and inextensible evolutions of timelike ruled surfaces.
result Exact solutions for the evolution equations of curvatures of spacelike curves.