arXiv research

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

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

Enhances trading signals using image analysis and weighted moving averages.

A new method for exponentially weighted moving models using approximations.

Improved averaging method for noisy observations converges strongly.

We introduce a covariance matrix estimator that both takes into account the heteroskedasticity of financial returns (by using an exponentially weighted moving average) and reduces the effective dimensionality of the estimation (and hence measurement noise) via techniques borrowed from random matrix theory. We calculate…

Extends double linear policy with time-varying weights and proves robust positive expectation.

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 …

The paper analyzes MACD using operator theory.

New method estimates robust multi-period portfolios using entropy.

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

We propose an explicit recursive method to approximate a power-law with a finite sum of weighted exponentials. Applications to moving averages with long memory are discussed in relationship with stochastic volatility models.

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 …

Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted M…

PACE optimizes training for averaged language models, improving performance.

In this note we discuss the mathematical tools to define trend indicators which are used to describe market trends. We explain the relation between averages and moving averages on the one hand and the so called exponential moving average (EMA) on the other hand. We present a lot of examples and give the definition of t…

BEMA reduces bias in EMA, leading to faster convergence and better performance.

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

Unified analysis of finite weight averaging methods in deep learning.

WAVE improves time series forecasting by integrating AR and MA components.

Improved diffusion models for image synthesis with better training dynamics.

Dynamic trading strategies, in the spirit of trend-following or mean-reversion, represent an only partly understood but lucrative and pervasive area of modern finance. Assuming Gaussian returns and Gaussian dynamic weights or signals, (e.g., linear filters of past returns, such as simple moving averages, exponential we…

Improving optimization for iterate-averaged language models

This paper provides an insight to the time-varying dynamics of the shape of the distribution of financial return series by proposing an exponential weighted moving average model that jointly estimates volatility, skewness and kurtosis over time using a modified form of the Gram-Charlier density in which skewness and ku…

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…

Conventional Monte Carlo simulations are stochastic in the sense that the acceptance of a trial move is decided by comparing a computed acceptance probability with a random number, uniformly distributed between 0 and 1. Here we consider the case that the weight determining the acceptance probability itself is fluctuati…

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…

Improved score-based models generate high-quality images up to 256x256.

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…

This paper develops a new theory for ensemble learning beyond variance reduction.

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…

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

The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, and penalizes predictions that are inconsistent with this target. However, because the targets change …

In several recently proposed stochastic optimization methods (e.g. RMSProp, Adam, Adadelta), parameter updates are scaled by the inverse square roots of exponential moving averages of squared past gradients. Maintaining these per-parameter second-moment estimators requires memory equal to the number of parameters. For …

A new accelerated method with simpler momentum update rules.

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

Pipeline for comparing trading algorithms in finance and crypto.

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

Machine learning models outperform traditional technical analysis in Bitcoin trading.

Adaptive t-distribution estimates nonstationary time series using moving moments.

This paper analyzes the skewness of momentum trading strategies.

In this paper we consider backward stochastic differential equations with time-delayed generators of a moving average type. The classical framework with linear generators depending on $(Y(t),Z(t))$ is extended and we investigate linear generators depending on $(\frac{1}{t}\int_0^tY(s)ds, \frac{1}{t}\int_0^tZ(s)ds)$. We…

Adaptive estimation of alpha-Stable distribution and Hurst exponent for nonstationary time series.

TT-DAC-PS: A deterministic actor-critic approach for optimal trade execution

Paper analyzes how EMA improves SGD in linear regression.

Paper explores weighted averaging schemes for SGD, achieving asymptotic normality and optimality.

This paper analyzes SGD with increasingly weighted averaging for optimization and generalization.

Bayesian inference for inverse problems using mean-shift interacting particles