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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.

168,742 papers · 148 categories

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316394125 · May 202619922001200920172026
48 results for trajectory growth

The so-called great divergence in the income per capita is described in the Unified Growth Theory as the mind-boggling and unresolved mystery about the growth process. This mystery has now been solved: the great divergence never happened. It was created by the manipulation of data. Economic growth in various regions is…

2016-03-28abs ↗pdf ↗

Mathematical method based on a direct or indirect analysis of growth rates is described. It is shown how simple assumptions and a relatively easy analysis can be used to describe mathematically complicated trends and to predict growth. Only rudimentary knowledge of calculus is required. Projected trajectories based on …

2017-04-27abs ↗pdf ↗

Growth rate of the world Growth Domestic Product (GDP) is analysed to determine possible pathways of the future economic growth. The analysis is based on using the latest data of the World Bank and it reveals that the growth rate between 1960 and 2014 was following a trajectory approaching asymptotically a constant val…

2015-10-26abs ↗pdf ↗

A simple but useful method of reciprocal values is introduced, explained and illustrated. This method simplifies the analysis of hyperbolic distributions, which are causing serious problems in the demographic and economic research. It allows for a unique identification of hyperbolic distributions and for unravelling co…

2017-08-29abs ↗pdf ↗

Data describing historical growth of income per capita [Gross Domestic Product per capita (GDP/cap)] for the world economic growth and for the growth in Western Europe, Eastern Europe, Asia, former USSR, Africa and Latin America are analysed. They follow closely the linearly-modulated hyperbolic distributions represent…

2016-03-05abs ↗pdf ↗

GLMM trees identify subgroups with different growth patterns in longitudinal data.

problem Identifying subgroups with distinct growth trajectories in longitudinal studies.
method Extended GLMM trees for longitudinal data.
result Extended GLMM trees outperform other methods in accuracy and speed.

Paper reduces sample complexity for bilinear systems identification to nearly constant.

problem Identifying discrete-time bilinear systems under bounded disturbances.
method Uses trajectory-dependent regressors and polynomial mean-square state growth analysis.
result Proves sample complexity of O~(1/ε)\widetilde{\mathcal O}(1/ε) for estimation error εε.

We consider the problem of finding optimal strategies that maximize the average growth-rate of multiplicative stochastic processes. For a geometric Brownian motion the problem is solved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction o…

2015-10-17abs ↗pdf ↗

We consider a vector field XX on a closed manifold which admits a Lyapunov one form. We assume XX has Morse type zeros, satisfies the Morse--Smale transversality condition and has non-degenerate closed trajectories only. For a closed one form ηη, considered as flat connection on the trivial line bundle, the differen…

2005-08-12abs ↗pdf ↗

Study adds memory effect to Solow-Swan model for more accurate economic growth modeling.

problem Inaccuracies in classical Solow-Swan model in capturing long-term dynamics.
method Introduced fractional calculus with Caputo derivative into Solow-Swan framework.
result Fractional-order model shows significant impact on capital accumulation and stability.

Study predicts U.S. county COVID-19 growth using demographic and social distancing data.

problem Predicting county-level COVID-19 growth during the pandemic.
method Spectral clustering, correlation matrix, demographic features, social distancing scores, LSTM model.
result Effective prediction of future county growth using demographic and social distancing data.

A tutorial on various methods for clustering longitudinal data.

problem Identifying groups with different trends in longitudinal data.
method Group-based trajectory modeling, growth mixture modeling, longitudinal k-means.
result Strengths, limitations, and model extensions of the methods are discussed.

Topological entropy measures the number of distinguishable orbits in a dynamical system, thereby quantifying the complexity of chaotic dynamics. One approach to computing topological entropy in a two-dimensional space is to analyze the collective motion of an ensemble of system trajectories taking into account how traj…

2018-08-30abs ↗pdf ↗

We consider vector fields XX on a closed manifold MM with rest points of Morse type. For such vector fields we define the property of exponential growth. A cohomology class ξH1(M;R)ξ\in H^1(M;\mathbb R) which is Lyapunov for XX defines counting functions for isolated instantons and closed trajectories. If XX has exponent…

2004-05-03abs ↗pdf ↗

Geometric Brownian motion (GBM) is a model for systems as varied as financial instruments and populations. The statistical properties of GBM are complicated by non-ergodicity, which can lead to ensemble averages exhibiting exponential growth while any individual trajectory collapses according to its time-average. A com…

2012-09-20abs ↗pdf ↗

The study analyzes the performance of a nonparametric estimator for dynamical systems.

problem Analyzing the performance of a nonparametric estimator for dynamical systems.
method Nonparametric least squares estimator (LSE) and information-theoretic methods.
result Rate-optimal error bounds for nonparametric hypotheses classes.

The study identifies conditions under which algorithmic stability explains generalization in interpolating learning systems.

problem Understanding when algorithmic stability explains generalization in interpolating learning systems.
method Modeling training as a function-space trajectory and measuring sensitivity to single-sample perturbations.
result There exist interpolating regimes with small risk where contractive sensitivity cannot hold, showing that stability is not a universal explanation.

This paper tackles robust growth maximization with stochastic factors, finding optimal strategies independent of the factor process.

problem Maximizing asymptotic growth under model uncertainty with stochastic factor processes.
method Combines techniques from partial differential equations, calculus of variations, and generalized Dirichlet forms.
result Optimal trading strategy is functionally generated and independent of the stochastic factor process.

EPD method accurately captures parameter distributions from RCS data.

problem Limitations of traditional methods in estimating parameter distributions from RCS data.
method EPD method generates synthetic trajectories, estimates parameters, and selects parameters based on discrepancy.
result EPD provides accurate distribution of parameters without data loss.

GAGA learns a warped metric for geometry-aware data generation and interpolation.

problem Challenges in generating data with meaningful geometry in high-dimensional datasets.
method Combines manifold learning with generative modeling to learn a warped Riemannian metric.
result GAGA improves trajectory inference by 30% in single-cell population-level data.

EB-VAE combines tumor growth and dropout data for personalized treatment response modeling.

problem Challenges in integrating longitudinal tumor measurements, dropout information, and genetic covariates.
method Extended EB-VAE framework to jointly model longitudinal and time-to-event data, incorporating dropout hazard and genetic covariates.
result Hybrid decoder formulation yields consistent treatment-effect parameters and prior predictive performance comparable to neural decoder.

Paper classifies economic states and optimizes portfolios for stagflationary environments.

problem Economic uncertainty and stagflationary conditions.
method Mathematical techniques for analyzing multivariate time series, economic driver analysis, self-similarity identification, and portfolio optimization.
result Constructs economic state classifications and computes economic state integrals.

In this paper, we study the system identification problem for sparse linear time-invariant systems. We propose a sparsity promoting block-regularized estimator to identify the dynamics of the system with only a limited number of input-state data samples. We characterize the properties of this estimator under high-dimen…

2018-03-21abs ↗pdf ↗

JFR-rg model explains Japan's stable debt despite high interest rates and low growth.

problem Understanding Japan's stable government debt despite high interest rates and low growth.
method Formalizes financial repression channels through JFR-rg model, incorporating financial repression bias and exchange-rate channel.
result Identifies Normalization Trap and Captive Financial System Parameter, showing debt dynamics under financial repression.

New findings show score matching's accuracy doesn't ensure numerical stability in diffusion sampling.

problem Numerical stability issues in diffusion sampling despite small forward-marginal error.
method Constructing a smooth score field with arbitrarily small forward-marginal L2L^2 error, showing nonexplosive behavior and moments of every order.
result Euler--Maruyama discretizations can converge in probability even when moments diverge, demonstrating failure of weak convergence.

New model predicts stock performance in large equity markets.

problem Predicting stock performance in large equity markets over long time horizons.
method Rank-based volatility stabilized models calibrated to empirical data.
result The model exhibits relative arbitrage and statistically fits empirical features.

The paper tackles transfer learning for growing matrix representations, improving estimation accuracy.

problem Structured matrix estimation under growing ambient dimensions and latent representations.
method Proposes a general transfer framework decomposing target parameters into embedded source components, low-rank innovations, and sparse edits. Develops an anchored alternating projection estimator.
result Establishes deterministic error bounds that separate target noise, representation growth, and source estimation error, yielding improved rates.

Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in such a way as to be able to perform a variety of tasks, like classifying images. Here we study the emergence of structure in the wei…

2019-02-21abs ↗pdf ↗

A new method models continuous-time counterfactual outcomes using neural controlled differential equations.

problem Estimating personalized healthcare outcomes over irregularly sampled data.
method Interpreting data as samples from a continuous-time process, modeling latent trajectory using controlled differential equations, and using adversarial training for time-dependent confounding.
result TE-CDE consistently outperforms existing approaches in irregularly sampled scenarios.

A higher-order Runge-Kutta optimizer performs poorly compared to Adam when evaluated fairly.

problem Evaluating the performance of adaptive Runge-Kutta optimizers under strict conditions.
method Built and evaluated a representative Adam variant using a Bogacki-Shampine 3(2) RK pair, FSAL reuse, and local-error step control.
result The adaptive nature of the RK optimizer is illusory; it behaves like a fixed-step optimizer with gradient averaging.

Gradient descent trains both layers of a ReLU network to fit a linear model.

problem Training dynamics of a ReLU network to fit a linear target function.
method Jointly training both layers of a one-hidden-layer ReLU network in a realizable setting with Gaussian inputs and labels.
result Gradient descent from a small random initialization converges to a global minimizer at a linear rate with optimal sample complexity.

We study the problem of discriminative sub-trajectory mining. Given two groups of trajectories, the goal of this problem is to extract moving patterns in the form of sub-trajectories which are more similar to sub-trajectories of one group and less similar to those of the other. We propose a new method called Statistica…

2019-05-06abs ↗pdf ↗

A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and…

2015-12-11abs ↗pdf ↗

The recent liberalization of the electricity and gas markets has resulted in the growth of energy exchanges and modelling problems. In this paper, we modelize jointly gas and electricity spot prices using a mean-reverting model which fits the correlations structures for the two commodities. The dynamics are based on Or…

2009-10-01abs ↗pdf ↗