Hybrid method reveals true currency correlations.
problem Identify currency status in foreign exchange networks.
method Combines DCCC and network deconvolution to filter indirect effects.
result Reflects currency status changes and is more stable.
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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.
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48 results for “detrended deconvolution”
A new method for joint noise removal and trend estimation from sparse signals.
problem Jointly removing noise and estimating trends from sparse signals.
method PENDANTSS combines SOOT/SPOQ penalties with BEADS algorithm in a Trust-Region block alternating variable metric forward-backward approach.
result Outperforms comparable methods in deconvolving analytical chemistry signals.
A novel Gaussian process approach for deconvolution of missing data signals.
problem Recovering a latent source from observations with missing parts and unknown filters.
method Gaussian process prior for Bayesian nonparametric deconvolution.
result The proposed Gaussian process deconvolution (GPDC) method is feasible and effective.
Deconvolutional layers have been widely used in a variety of deep models for up-sampling, including encoder-decoder networks for semantic segmentation and deep generative models for unsupervised learning. One of the key limitations of deconvolutional operations is that they result in the so-called checkerboard problem.…
Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimationphysics.data-an
Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.
problem Challenges in extracting meaningful peaks from noisy or complex spectra.
method Bayesian spectral deconvolution coupled with a physical-property regression layer.
result Recovery of weak peaks in poly(lactic acid) IR spectra related to degradation rates.
Detrended Cross-Correlation Analysis: A New Method for Analyzing Two Non-stationary Time Seriesq-fin.ST
Here we propose a method, based on detrended covariance which we call detrended cross-correlation analysis (DXA), to investigate power-law cross-correlations between different simultaneously-recorded time series in the presence of non-stationarity. We illustrate the method by selected examples from physics, physiology,…
Study uses detrended cross-correlation to analyze cryptocurrency market, revealing robust collective modes and distinguishing interdependencies.
problem Nonstationarity, long-range memory, and heavy-tailed fluctuations obscure traditional correlations in complex systems.
method Constructs detrended correlation matrices using multifractal detrended cross-correlation coefficient to emphasize different fluctuations.
result Detrending and fluctuation analysis reveal distinct spectral properties from random case, identifying market and sectoral components.
In image deconvolution problems, the diagonalization of the underlying operators by means of the FFT usually yields very large speedups. When there are incomplete observations (e.g., in the case of unknown boundaries), standard deconvolution techniques normally involve non-diagonalizable operators, resulting in rather …
Normalizing flows improve density estimation from noisy data.
problem Estimating underlying density from noisy samples.
method Use normalizing flows for density estimation with arbitrary noise distributions, using amortized variational inference.
result Normalizing flows can outperform Gaussian mixtures for density deconvolution.
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties. Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in missi…
Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We investigate in this paper how Deep Learning (DL) could be used to perform this task…
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data. In this work, we show that this redund…
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 determining the position of the detrending window. We develop multifractal detrending moving a…
Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuo…
A new method for accurately reconstructing signals without knowing the kernel or signal regularity.
problem Recovering signals from noisy measurements without prior knowledge of the convolution kernel or signal regularity.
method Parametrizing the convolution kernel and prior length-scales, jointly estimated in the inversion procedure.
result Accurate reconstructions of signals with varying regularity and unknown kernel size.
Detrended fluctuation analysis (DFA) is a simple but very efficient method for investigating the power-law long-term correlations of non-stationary time series, in which a detrending step is necessary to obtain the local fluctuations at different timescales. We propose to determine the local trends through empirical mo…
OmniFold uses deep learning to deconvolve high-dimensional simulations.
problem Removing detector distortions and accounting for noise processes in high-dimensional simulations.
method OmniFold is a deep learning-based approach for maximum likelihood deconvolution.
result OmniFold can remove detector distortions and account for noise processes and acceptance effects.
This paper develops a new method for online density estimation from noisy data.
problem Estimating probability density function from noisy streaming data.
method Quasi-Bayesian sequential deconvolution using Newton's algorithm.
result Sequential deconvolution estimate with large sample asymptotic guarantees.
New algorithm profiles nuisance parameters for multidimensional deconvolution.
problem Statistical removal of instrumental effects in experimental measurements.
method Iterative algorithm called Profile OmniFold that profiles nuisance parameters.
result Illustrated with a Gaussian example, highlighting promising capabilities.
Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFMphysics.data-an
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind deconvolution problem, prior distributions are specified for the PSF and the 3D image. Joint image reconstruction and PSF…
Deconvolution microscopy has been extensively used to improve the resolution of the widefield fluorescent microscopy. Conventional approaches, which usually require the point spread function (PSF) measurement or blind estimation, are however computationally expensive. Recently, CNN based approaches have been explored a…
Nonparametric density deconvolution and denoising using simulation-based inference
problem Learning latent signals and their distributions in the presence of measurement noise
method Convolutional maximum mean discrepancy (convMMD) loss and likelihood-free framework
result Learn a latent generative model matching observed data distribution
A generative Bayesian model is developed for deep (multi-layer) convolutional dictionary learning. A novel probabilistic pooling operation is integrated into the deep model, yielding efficient bottom-up and top-down probabilistic learning. After learning the deep convolutional dictionary, testing is implemented via dec…
New algorithm for tensor decomposition and Gaussian mixture models.
problem Efficiently decompose overcomplete order-3 tensors and estimate parameters of Gaussian mixtures.
method Proposes Jennrich's algorithm adapted for tensor decomposition and Gaussian mixture models.
result Efficient algorithm for decomposing symmetric overcomplete order-3 tensors and estimating parameters of Gaussian mixtures.
Blind deconvolution is a ubiquitous problem of recovering two unknown signals from their convolution. Unfortunately, this is an ill-posed problem in general. This paper focuses on the {\em short and sparse} blind deconvolution problem, where the one unknown signal is short and the other one is sparsely and randomly sup…
The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as…
DDN models flexible free-form conditional distributions.
problem Difficulty in explicitly approximating arbitrary conditional distributions.
method Deconvolutional neural network framework for discretizing continuous domains.
result DDN outperforms other density-estimation methods on various tasks.
Single-channel signal separation and deconvolution aims to separate and deconvolve individual sources from a single-channel mixture and is a challenging problem in which no prior knowledge of the mixing filters is available. Both individual sources and mixing filters need to be estimated. In addition, a mixture may con…
Study characterizes spike deconvolution basin for noisy data.
problem Recover spike locations from noisy convolution with PSF across multiple snapshots.
method Variable-projection formulation, explicit basin of convexity characterization, local convergence guarantees.
result Consistent estimator within basin of convexity under stochastic noise, complementary error bound under adversarial noise.
Local minimax analysis for Poisson deconvolution of discrete signals.
problem Estimating a discrete uniform signal from Poisson convolutions.
method Local minimax risk analysis of a broad class of kernels.
result Sharp estimation rates as a function of signal clustering.
Detrended fluctuation analysis as a regression framework: Estimating dependence at different scalesq-fin.ST
We propose a framework combining detrended fluctuation analysis with standard regression methodology. The method is built on detrended variances and covariances and it is designed to estimate regression parameters at different scales and under potential non-stationarity and power-law correlations. The former feature al…
We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving A…
Intertrade duration of equities is an important financial measure characterizing the trading activities, which is defined as the waiting time between successive trades of an equity. Using the ultrahigh-frequency data of a liquid Chinese stock and its associated warrant, we perform a comparative investigation of the sta…
Two-stage nonconvex algorithm and convex relaxation both achieve optimal accuracy in noisy blind deconvolution.
problem Solving bilinear systems of equations with random noise under different designs.
method Two-stage nonconvex algorithm and convex relaxation.
result Both methods achieve minimax-optimal accuracy in the presence of random noise.
Image super-resolution is a challenging task and has attracted increasing attention in research and industrial communities. In this paper, we propose a novel end-to-end Attention-based DenseNet with Residual Deconvolution named as ADRD. In our ADRD, a weighted dense block, in which the current layer receives weighted f…
Tissue heterogeneity is a major confounding factor in studying individual populations that cannot be resolved directly by global profiling. Experimental solutions to mitigate tissue heterogeneity are expensive, time consuming, inapplicable to existing data, and may alter the original gene expression patterns. Here we a…
Multifractal time series analysis is a approach that shows the possible complexity of the system. Nowadays, one of the most popular and the best methods for determining multifractal characteristics is Multifractal Detrended Fluctuation Analysis (MFDFA). However, it has some drawback. One of its core elements is detrend…
Cryptocurrency market becomes more cross-correlated over time.
problem Understanding the inter-market dependencies of cryptocurrencies.
method Time series analysis, spectral analysis, topological analysis of minimal spanning trees.
result Cryptocurrency market becomes more cross-correlated with other markets over time.
Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuationsphysics.data-an
The detrended cross-correlation coefficient has recently been proposed to quantify the strength of cross-correlations on different temporal scales in bivariate, non-stationary time series. It is based on the detrended cross-correlation and detrended fluctuation analyses (DCCA and DFA, respectively) and c…
We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated `high …
The performance of the multifractal detrended analysis on short time series is evaluated for synthetic samples of several mono- and multifractal models. The reconstruction of the generalized Hurst exponents is used to determine the range of applicability of the method and the precision of its results as a function of t…
A Multifractal Detrended Fluctuation Description of Iranian Rial-US Dollar Exchange Ratephysics.data-an
The miltifractal properties and scaling behaviour of the exchange rate variations of the Iranian rial against the US dollar from a daily perspective is numerically investigated. For this purpose the multifractal detrended fluctuation analysis (MF-DFA) is used. Through multifractal analysis, the scaling exponents, gener…
Deconvoluting Kernel Density Estimation and Regression for Locally Differentially Private Datamath.ST
Paper tackles privacy-preserving data density issues using deconvolution.
problem Privacy-preserving noise affects data density, leading to under/over-estimation.
method Develops deconvoluting kernel density estimators and regression models.
result Demonstrates improved accuracy in estimating heavy-hitters with locally differential data.
The intraday pattern, long memory, and multifractal nature of the intertrade durations, which are defined as the waiting times between two consecutive transactions, are investigated based upon the limit order book data and order flows of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in 2003. An inverse…
Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time seriesphysics.data-an
Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different genera…
It is ubiquitous in natural and social sciences that two variables, recorded temporally or spatially in a complex system, are cross-correlated and possess multifractal features. We propose a new method called multifractal detrended cross-correlation analysis (MF-DXA) to investigate the multifractal behaviors in the pow…
An evolutionary algorithm separates mixed DNA profiles in forensic genetics.
problem Deconvolving mixed DNA profiles from crime samples.
method Multiple population evolutionary algorithm (MEA) with guided mutation.
result The MEA successfully deconvoluted DNA profiles from crime samples.
Beurling-Selberg Extremization for Dual-Blind Deconvolution Recovery in Joint Radar-Communicationscs.IT
Optimal joint separation condition for radar and communications channels in dual-blind deconvolution.
problem Recovering information from overlaid radar and communications signals with unknown channels.
method Extremal functions from Beurling-Selberg interpolation theory for joint separation, nuclear norm minimization for matrix retrieval, and MUSIC for parameter estimation.
result Guaranteed well-conditioned Vandermonde matrix for MUSIC, validating theoretical findings.