arXiv research

New method improves inference in deep Gaussian processes.

Simple soft sensor models improve prediction accuracy with small moving windows.

Auto-regressive models improve smoothing efficiency with exponentially tapered windows.

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

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

Study improves portfolio optimization for Indonesian banks using robust methods.

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…

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

A new method for exponentially weighted moving models using approximations.

BAWS adapts window size for financial risk forecasting.

This paper models dynamic dependence in financial time series for risk measurement.

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 detrending moving average (DMA) algorithm is a widely used technique to quantify the long-term correlations of non-stationary time series and the long-range correlations of fractal surfaces, which contains a parameter $θ$ determining the position of the detrending window. We develop multifractal detrending moving a…

We study the dynamic evolution of cross-correlations in the Chinese stock market mainly based on the random matrix theory (RMT). The correlation matrices constructed from the return series of 367 A-share stocks traded on the Shanghai Stock Exchange from January 4, 1999 to December 30, 2011 are calculated over a moving …

Research predicts cryptocurrency staking rewards with high accuracy.

Graphical estimation of count time series dependencies.

Historical (Stressed-) Value-at-Risk ((S)VAR), and Expected Shortfall (ES), are widely used risk measures in regulatory capital and Initial Margin, i.e. funding, computations. However, whilst the definitions of VAR and ES are unambiguous, they depend on input distributions that are data-cleaning- and Data-Model-depende…

New algorithm FEWA tackles rotting bandits with improved regret bounds.

CGPCM models causally-generated signals with improved inference.

The cluster analysis methods are used in order to perform a comparative study of 15 EU countries in relation with the fluctuations of some basic macroeconomic indicators. The statistical distances between countries are calculated for various moving time windows, and the time variation of the mean statistical distance i…

Simple GBRT model improved by window-based input transformation outperforms state-of-the-art deep learning models.

New method estimates animal density using acoustic data, accounting for unknown call identities.

Adaptive beamforming collapses in highly non-stationary environments, but the Universal Switching Beamformer resolves this by dynamically adjusting memory length.

Acoustic sensors identify vehicles using spectral embedding.

FOCuS detects changes in mean from high-frequency data efficiently.

This study optimizes trading strategy parameters using walk-forward techniques and finds robust performance.

The paper develops a neural network-based method for detecting change points in large-scale time-evolving data.

A new method corrects bias in machine learning for trading by filtering out non-executable prices.

Optimal weight windows are symmetric rectangles centered at peak.

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic {\em unpooling} is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-…

The Black-Scholes theory of option pricing has been considered for many years as an important but very approximate zeroth-order description of actual market behavior. We generalize the functional form of the diffusion of these systems and also consider multi-factor models including stochastic volatility. Daily Eurodoll…

Automatically identifies vehicles from audio sensors without needing labeled data.

The study improves VaR forecast accuracy by modeling conditional quantile dynamics.

Differentiable Window improves attention modules by enabling more focused attentions.

Financial data has been extensively studied for correlations using Pearson's cross-correlation coefficient ρ as the point of departure. We employ an estimator based on recurrence plots --- the Correlation of Probability of Recurrence (CPR) --- to analyze connections between nine stock indices spread worldwide. We sugge…

A new method selects regions of interest in GC-MS data without prior target selection.

Develops an efficient online robust PCA method for big data.

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dy…

Financial markets are complex adaptive systems, and are commonly studied as complex networks. Most of such studies fall short in two respects: they do not account for non-linearity of the studied relationships, and they create one network for the whole studied time series, providing an average picture of a very long, e…

This paper studies the 28 time series of Libor rates, classified in seven maturities and four currencies), during the last 14 years. The analysis was performed using a novel technique in financial economics: the Complexity-Entropy Causality Plane. This planar representation allows the discrimination of different stocha…

We propose a new volatility model based on two stylized facts of the volatility in the stock market: clustering and leverage effect. We calibrate our model parameters, in the leading order, with 77 years Dow Jones Industrial Average data. We find in the short time scale (10 to 50 days) the future volatility is sensitiv…

Study compares GARCH, EWMA, and IV models for GBP/USD and EUR/GBP currency pairs.

ARIMA model shows promise for short-term Bitcoin price prediction but fails for long-term predictions.

Study examines how institutional differences and crises affect volatility in ASEAN stock markets.

Study fusion methods for financial image views to improve robustness against attacks.

Entropy measure assesses market volatility and price heterogeneity.

Hybrid pipeline detects spike-and-wave discharges in long-term EEG recordings.

Paper proposes efficient AUC estimation in sliding windows.