arXiv research

New method predicts spatio-temporal data with short and long-range dependence.

Paper proposes a mean-field gradient descent for zero-sum games, proving convergence to Nash equilibrium.

The study explores mixed Killing vector fields on almost coKähler manifolds.

New methods for delta-moves on algebraically split links identified.

We consider the mixed ray transform of tensor fields on a three-dimensional compact simple Riemannian manifold with boundary. We prove the injectivity of the transform, up to natural obstructions, and establish stability estimates for the normal operator on generic three dimensional simple manifold in the case of 1+1 a…

Explains the difference between EMA and moving EMA, focusing on market trend indicators.

We show Vector Autoregressive Moving Average models with scalar Moving Average components could be estimated by generalized least square (GLS) for each fixed moving average polynomial. The conditional variance of the GLS model is the concentrated covariant matrix of the moving average process. Under GLS the likelihood …

New moving average adapts weight dynamically based on polynomial and wavefunction.

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging becau…

This study uses moving average cluster entropy to analyze financial market dynamics.

New algorithm for solving minimax problems over distributions converges to Nash equilibrium.

A new method normalizes flow mixtures for better inference across different data types.

We develop variation formulas for the quantities of extrinsic geometry for adapted variations of metrics on almost-product (e.g. foliated) Riemannian manifolds, and apply them to study the total mixed scalar curvature of a distribution -- analogue of the classical Einstein-Hilbert action. The mixed scalar curvature ${\…

ARMA nets expand receptive fields for dense prediction tasks.

In the paper, we introduce a new measure of correlation between possibly non-stationary series. As the measure is based on the detrending moving-average cross-correlation analysis (DMCA), we label it as the DMCA coefficient $ρ_{DMCA}(λ)$ with a moving average window length $λ$. We analytically show that the coefficient…

The Hurst exponent $H$ of long range correlated series can be estimated by means of the Detrending Moving Average (DMA) method. A computational tool defined within the algorithm is the generalized variance $σ_{DMA}^2={1}/{(N-n)}\sum_i [y(i)-\widetilde{y}_n(i)]^2\:$, with $\widetilde{y}_n(i)= {1}/{n}\sum_{k}y(i-k)$ the…

The present research work proposes a new fast fixed-point averaging algorithm on the compact Stiefel manifold based on a mixed retraction/lifting pair. Numerical comparisons between fixed-point algorithms based on the proposed non-associated retraction/lifting map pair and two associated retraction/lifting pairs confir…

The possibility that price dynamics is affected by its distance from a moving average has been recently introduced as new statistical tool. The purpose is to identify the tendency of the price dynamics to be attractive or repulsive with respect to its own moving average. We consider a number of tests for various models…

We propose a method for pricing American options whose pay-off depends on the moving average of the underlying asset price. The method uses a finite dimensional approximation of the infinite-dimensional dynamics of the moving average process based on a truncated Laguerre series expansion. The resulting problem is a fin…

The paper analyzes MACD using operator theory.

Lower bound on BART's mixing time increases with data points.

Multiple seasonal patterns play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the disti…

New method assesses financial and cyber risks under uncertainty.

Paper predicts cryptocurrency bull and bear phases using Bitcoin's moving averages.

New algorithm learns optimal policy for average reward MDPs with sample complexity matching lower bound.

Bayesian framework mixes imperfect models for improved predictions.

Study on mixed Killing vector fields on Cigar Ricci-Bourguignon solitons.

A new MFG framework for evolving clusters from Gaussian mixtures.

In this paper we describe braid equivalence for knots and links in a 3-manifold $M$ obtained by rational surgery along a framed link in $S^3$. We first prove a sharpened version of the Reidemeister theorem for links in $M$. We then give geometric formulations of the braid equivalence via mixed braids in $S^3$ using the…

New RL method MAC improves performance in sparse reward settings.

The paper extends knot polynomials to annular and toroidal pseudo links.

PACE optimizes training for averaged language models, improving performance.

Optimal weight windows are found by projecting the origin onto a convex polytope.

Let $V_i$ be a finite dimensional Hermitian vector space of holomorphic sections of a line bundle $L_i$ on a complex $n$-dimensional manifold $X$. We associate to $V_i$ the non-negative Hermitian quadratic form $g_i$ on $X,$ define a Hermitian mixed volume of $X$ for a "mixing tuple" of $n$ non-negative Hermitian forms…

Efficient method for pricing Bermudan moving average options using GPR-GHQ.

One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions,…

Defines observer-invariant time derivatives on moving surfaces.

In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…

We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …

Time series analysis is a key component of machine learning, with applications in various fields.

The paper highlights the importance of model discrepancy in cardiac simulations.

Machine learning models outperform traditional technical analysis in Bitcoin trading.

The Cayley--Salmon theorem implies the existence of a 27-sheeted covering space specifying lines contained in smooth cubic surfaces over $\mathbb{C}$. In this paper we compute the rational cohomology of the total space of this cover, using the spectral sequence in the method of simplicial resolution developed by Vassil…

Improved diffusion models for image synthesis with better training dynamics.

TD(0) with Polyak-Ruppert averaging achieves robust and fast convergence rates

Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how p…

Enhances MMSB for complex graph structures with HL-MRF priors.

Magnetic manifold HMC improves sampling on constrained manifolds.