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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.

168,695 papers · 148 categories

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36811 · May 202619922001200920172026
48 results for Southern Oscillation

Graph neural networks improve El Niño forecasts.

problem Improving seasonal forecasting accuracy for El Niño-Southern Oscillation.
method Designing a novel graph connectivity learning module to model large-scale spatial interactions with ENSO forecasting.
result Our model \graphino outperforms state-of-the-art models for forecasts up to six months ahead.

Proposes a new method combining Reservoir Computing and Normalizing Flow for predicting stochastic dynamical systems.

problem Predicting and capturing long-term behaviors of stochastic dynamical systems.
method Data-driven framework combining Reservoir Computing and Normalizing Flow, integrating error modeling and both approaches virtues.
result Successfully predicts the long-term evolution of stochastic dynamical systems and replicates dynamical behaviors.

Study improves seasonal forecasts using deep learning.

problem Challenges in generating large forecast ensembles and limited observations for verification.
method Developed a probabilistic deep neural network model.
result Demonstrated favorable skill compared to state-of-the-art dynamical forecast systems.

Paper models spatio-temporal extremes using conditional variational autoencoders.

problem Modeling co-occurrence of extreme weather events under changing climate conditions.
method Conditional Variational Autoencoder (cXVAE) with CNN integration.
result Accurately emulates spatial fields and recovers extremal dependence with low computational cost.

The study uses machine learning to predict CAT bond coupons based on climate data.

problem Predicting CAT bond coupons using climate data.
method Combining climate indicators with machine learning models (random forest, gradient boosting, etc.).
result Extremely randomized trees achieved the lowest RMSE in predicting CAT bond coupons.

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few p…

2019-06-17abs ↗pdf ↗

Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for more than a few predictors which has limited their application mostl…

2015-06-18abs ↗pdf ↗

M-CaStLe discovers causal structures in multivariate space-time data.

problem Challenges in causal graph discovery for high-dimensional gridded data.
method Generalizes CaStLe to multivariate analyses, using local embeddings and pooling spatial replicates.
result More accurately recovers multivariate causal structure and identifies physical dynamics.

GOAL algorithm reduces and rotates feature space for small data classification.

problem Challenges in identifying important features for classification in small data settings.
method GOAL algorithm reduces and rotates feature space in a lower-dimensional gauge, providing an analytically tractable solution.
result GOAL algorithm outperforms state-of-the-art ML tools in synthetic and real-world applications.

Study examines boundedness of oscillating singular integrals on specific Lie groups.

problem Investigating boundedness of oscillating singular integrals on Lie groups of polynomial growth.
method Presented kernel criteria in terms of sub-Riemannian structure and Fourier analysis.
result Extended classical oscillating conditions for boundedness of oscillating convolution operators.

Log-periodic oscillations have been used to predict price trends and crashes on financial markets. So far two types of log-periodic oscillations have been associated with the real markets. The first type are oscillations which accompany a rising market and which ends in a crash. The second type oscillations, called "an…

2003-07-14abs ↗pdf ↗

Estimates box dimension of fractal interpolation surfaces using oscillation vectors.

problem Estimating the complexity of fractal interpolation surfaces.
method Defined vertical scaling matrices and used them to relate oscillation vectors of different levels.
result Obtained the box dimension of generalized affine fractal interpolation surfaces.

The Duffing oscillator's parameters are identified online using variational message passing.

problem Estimating parameters of a nonlinear Duffing oscillator in real-time.
method Variational message passing on a factor graph of the Duffing oscillator's generative model.
result The online inference procedure performs as well as offline methods.

The present paper introduces a majority orienting model in which the dealers' behavior changes based on the influence of the price to show the oscillation of stock price in the stock market. We show the oscillation of the price for the model by applying the van der Pol equation which is a deterministic approximation of…

2004-03-31abs ↗pdf ↗

The study derives generalization bounds for neural oscillators, improving their performance with regularization.

problem Quantifying the generalization capacities of neural oscillators.
method Using Rademacher complexity and squared Wasserstein-1 distances, the study derives theoretical upper PAC generalization bounds for neural oscillators.
result Theoretical bounds show polynomial growth in estimation errors with MLP size and time length, and regularization improves performance.

Study finds the spectrum of a cubic Dirac operator on specific oscillator group manifolds.

problem Determining the spectrum of a cubic Dirac operator on oscillator group manifolds.
method Explicit decomposition of the regular representation and calculation of eigenspaces.
result Explicit eigenspaces and spectrum of the cubic Dirac operator determined.

Study on black hole interiors with matter fields, showing oscillation condition impacts blow-up.

problem Examining Strong Cosmic Censorship in the presence of matter fields.
method Einstein equations coupled with charged/massive scalar fields, spherically symmetric data, relaxation rate analysis.
result Oscillation condition on event horizon determines whether matter fields blow up or not.

Using geometric quantization procedure, the quantization of algebra of observables for physical system with Ricci-flat phase space is obtained. In the classical case the appointed physical system is reduced to harmonic oscillator when the one real parameter is vanished.

1999-02-18abs ↗pdf ↗

This manuscript served as lecture notes for a mini-course in the 2016 Southern California Geometric Analysis Seminar Winter School. The goal is to give a quick introduction to Kahler geometry by describing the recent resolution of Tian's three influential properness conjectures in joint work with T. Darvas. These resul…

2018-07-02abs ↗pdf ↗

New non-separable covariance kernels for spatiotemporal data derived from harmonic oscillator physics.

problem Capturing complex spatiotemporal dependencies in Gaussian processes.
method Hybrid spectral method based on the harmonic oscillator, deriving explicit covariance kernels.
result Explicit non-separable covariance kernels with space-time interactions.

Large learning rates cause oscillations in NN weights that improve generalization.

problem Improving generalization of neural networks trained with large learning rates.
method Theoretical analysis and feature-noise data generation model.
result Oscillating SGD with large learning rates benefits NN generalization by effectively learning weak features.

In a complex system, the interactions between individual agents often lead to emergent collective behavior like spontaneous synchronization, swarming, and pattern formation. The topology of the network of interactions can have a dramatic influence over those dynamics. In many studies, researchers start with a specific …

2019-05-04abs ↗pdf ↗

We show that there are minimal graphs in R^{n+1} whose intersection with the portion of the horizontal hyperplane contained in the unit ball has any prescribed geometry, up to a small deformation. The proof hinges on the construction of minimal graphs that are almost flat but have small oscillations whose geometry we c…

2016-02-16abs ↗pdf ↗

A new RNN model based on coupled oscillators mitigates gradient issues.

problem Gradient vanishing and exploding issues in RNNs.
method Time-discretization of a system of second-order ODEs modeling coupled oscillators.
result The model maintains bounded gradients, leading to stable learning of long-term dependencies.

KuramotoGNN uses Kuramoto model to prevent over-smoothing in graph neural networks.

problem Over-smoothing in graph neural networks where node features become indistinguishable.
method Integrates Kuramoto model to prevent phase synchronization and instead achieve frequency synchronization.
result KuramotoGNN reduces over-smoothing on various graph deep learning tasks.

The Black-Scholes model anticipates rather well the observed prices for options in the case of a strike price that is not too far from the current price of the underlying asset. Some useful extensions can be obtained by an adequate modification of the coefficients in the Black-Scholes equation. We investigate from a ma…

2013-10-15abs ↗pdf ↗

The main result is a version of Morse inequalities for the minimum and maximum ideal boundary conditions of the de Rham complex on strata of compact Thom-Mather stratifications, endowed with adapted metrics. An adaptation of the analytic method of Witten is used in the proof, as well as certain perturbation of the harm…

2012-05-02abs ↗pdf ↗

Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underly…

2013-03-20abs ↗pdf ↗

This paper extends the earlier work on an oscillating error correction technique. Specifically, it extends the design to include further corrections, by adding new layers to the classifier through a branching method. This technique is still consistent with earlier work and also neural networks in general. With this ext…

2017-11-19abs ↗pdf ↗