New method detects range of correlated fluctuations in time series.
problem Limited applicability of DCCA for amplitude information.
method Extended DCCA using multifractal DFA and DCCA.
result Identifies range of detrended fluctuation amplitudes.
Wavelet analysis reveals limitations in detecting multifractality in signals with isolated singularities.
problem Detecting multifractality in signals with isolated singularities using detrended fluctuation analysis and wavelet leaders.
method Comparison of detrended fluctuation analysis and wavelet leaders on signals with isolated singularities.
result Signals with isolated singularities can artefactually give rise to broad multifractal spectra, leading to incorrect inference of multifractality.
Matrix H-theory models stock market fluctuations using hierarchical multivariate distributions.
problem Understanding collective behavior in stock market fluctuations.
method Matrix H-theory framework for multivariate stochastic processes with hierarchical structure.
result Matrix H-theory effectively describes stock market fluctuations using Meijer G-functions.
Spectral clustering performance depends on eigenvector fluctuations, shown to be Gaussian.
problem Predicting the performance of spectral clustering.
method General spike random matrix model and rotational invariance of noise.
result Fluctuations of eigenvector entries are Gaussian in large-dimensional regime.
Novel adaptive filter detects multiple faults in noisy OTDR profiles.
problem Detecting meaningful level shifts from signal fluctuations in noisy OTDR profiles.
method Two-stage regularization filtering with parameter-free algorithm.
result Most adequate technique for fast detection of multiple unknown level-shifts in noisy OTDR profiles.
Study uses ML to forecast ionospheric scintillation severity.
problem Predicting amplitude scintillation severity using real-time data.
method Developed and tested six ML models, XGBoost being the most effective.
result XGBoost model achieved 77% prediction accuracy for scintillation severity.
Apparently random financial fluctuations often exhibit varying levels of complexity, chaos. Given limited data, predictability of such time series becomes hard to infer. While efficient methods of Lyapunov exponent computation are devised, knowledge about the process driving the dynamics greatly facilitates the complex…
Model captures neural responses influenced by unknown modulatory signals.
problem Fluctuations in modulatory factors confound neural response analysis.
method Developed a modulated Poisson model with known and unknown modulatory elements, constrained latent signals to be smooth in time, and used evidence optimization for fitting.
result Integrating out latent modulators yields better receptive field estimates.
We propose a novel algorithm - Multifractal Cross-Correlation Analysis (MFCCA) - that constitutes a consistent extension of the Detrended Cross-Correlation Analysis (DCCA) and is able to properly identify and quantify subtle characteristics of multifractal cross-correlations between two time series. Our motivation for …
We provide an alternative method for analysis of multifractal properties of time series. The new approach takes into account the behaviour of the whole multifractal profile of the generalized Hurst exponent h(q) for all moment orders q, not limited only to the edge values of h(q) describing in MFDFA scaling prope…
We discuss a simple model based on the Minority Game which reproduces the main stylized facts of anomalous fluctuations in finance. We present the analytic solution of the model in the thermodynamic limit and show that stylized facts arise only close to a line of critical points with non-trivial properties. By a simple…
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 ρr to emphasize different fluctuations. result Detrending and fluctuation analysis reveal distinct spectral properties from random case, identifying market and sectoral components.
Deep learning detects sleep state fluctuations in neonates from single EEG channel.
problem Monitoring sleep state fluctuations in neonatal intensive care units.
method Deep learning-based algorithm trained on 53 EEG recordings, validated on 30 polysomnography recordings.
result High accuracy (90%) in detecting quiet sleep states from single EEG channel, generalizing well to external dataset.
We investigate the presence of residual multifractal background for monofractal signals which appears due to the finite length of the signals and (or) due to the long memory the signals reveal. This phenomenon is investigated numerically within the multifractal detrended fluctuation analysis (MF-DFA) for artificially g…
The paper compares Bayesian uncertainty to MAP estimator in random features regression.
problem Comparing Bayesian uncertainty to MAP estimator in random features regression.
method Analyzing the variance of the posterior predictive distribution and comparing it to the risk of the MAP estimator.
result Asymptotic agreement between Bayesian uncertainty and MAP estimator under specific signal-to-noise ratios and sample sizes.
Investor optimizes stock investments with noisy future price signals.
problem Optimizing stock investments with uncertain future stock prices.
method Dynamic investment strategy with partial observation of Brownian motion.
result Closed-form solution for optimal investment problem.
Model allocates portfolios based on multifractal cross-correlations across different scales.
problem Heterogeneous scales and amplitude-dependent financial correlations.
method Constructs a portfolio allocation model using multifractal cross-correlation analysis (MFCCA) with signed fluctuation functions.
result Reduces tail risk and improves risk-adjusted performance compared to mean-variance model.
Predicting alt-coin prices with Twitter sentiment analysis.
problem Predicting price fluctuations of alt-coins using social media sentiment.
method Extracted hourly tweets, classified sentiment, created sentiment indices, trained a Gradient Boosting Tree Model.
result Model predictions correlated 0.81 with historical data, statistically significant.
Deep networks with orthogonal weights show stable fluctuations, improving generalization and training speed.
problem Fluctuations in deep networks with Gaussian weights can impair training, especially in networks with depth comparable to width.
method Analytical and numerical studies of fully-connected networks with orthogonal weight initialization and tanh activations.
result Rectangular networks with orthogonal weights have stable fluctuations independent of network depth, leading to better generalization and training speed.
For many externally driven complex systems neither the noisy driving force, nor the internal dynamics are a priori known. Here we focus on systems for which the time dependent activity of a large number of components can be monitored, allowing us to separate each signal into a component attributed to the external drivi…
Study uses PPG signals for detecting speech events and speaker characteristics.
problem Detecting speech events and speaker characteristics from PPG signals.
method End-to-end convolutional neural network architectures for gender and person verification.
result Promising results showing potential of PPG for speech processing tasks.
Paper proposes SERT model for US stock pricing, outperforming standard models during market shocks.
problem Capturing patterns of temporal sparsity in asset pricing during market fluctuations.
method Introduces SERT model based on pre-trained Transformer, compares with standard models in three periods.
result SERT model achieves highest out-of-sample R2 (11.94\% and 11.47\%) during extreme market fluctuations. Study on fluctuations in neural network kernels and predictions, focusing on finite width effects.
problem Characterizing fluctuations in finite width neural networks.
method Dynamical mean field theory analysis of wide but finite feature learning neural networks.
result Fluctuations in kernels and predictions are dynamically coupled, leading to reduced variance in feature learning regimes.
Signals consisting of a sequence of pulses show that inherent origin of the 1/f noise is a Brownian fluctuation of the average interevent time between subsequent pulses of the pulse sequence. In this paper we generalize the model of interevent time to reproduce a variety of self-affine time series exhibiting power spec…
Complex systems' systemic risk linked to frustration in network structure.
problem Understanding systemic risk in complex systems.
method Analysis of fluctuation correlations and network structure evolution.
result Emergence of frustration signals systemic risk in complex systems.
We extend our previous study of scaling range properties done for detrended fluctuation analysis (DFA) \cite{former_paper} to other techniques of fluctuation analysis (FA). The new technique called Modified Detrended Moving Average Analysis (MDMA) is introduced and its scaling range properties are examined and compared…
This paper uses social signals to improve cryptocurrency price forecasting.
problem Improving cryptocurrency price forecasting using social signals.
method LSTMs trained on historical price data and social data from GitHub and Reddit.
result Social signals reduce error in forecasting cryptocurrency prices, especially for Bitcoin.
In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links' weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link's weights) in a spatially embedde…
Study predicts hearing recovery in MD patients using TEOAE signals.
problem Predicting hearing recovery in MD patients during acute episodes.
method Applied machine learning to TEOAE signals from MD patients, using SVM for classification.
result Baseline TEOAE parameters can predict hearing recovery in MD patients.
A new method for audio denoising using deep neural networks.
problem Improving audio quality by removing background noise.
method Combines time and time-frequency domain processing; trains a deep neural network to fit the signal.
result The method effectively disentangles clean audio from noisy signals.
New method estimates Fourier transforms from finite data without periodicity assumptions.
problem Estimating Fourier transforms from discrete data points without periodicity assumptions.
method Gaussian process regression with gradient ascent method to estimate covariance function.
result Sharp and precise estimation of spectral density in noise-free and noisy signals.
What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we foc…
The cross-correlations between the exchange rate fluctuations of 74 currencies over the period 1995-2012 are analyzed in this paper. The eigenvalue distribution of the cross-correlation matrix exhibits a bulk which approximately matches the bounds predicted from random matrices constructed using mutually uncorrelated t…
A new method uses q-dependent MSTs to analyze stock market correlations.
problem Analyzing correlations between different fluctuation amplitudes and time scales.
method Introduces q-dependent minimum spanning trees (qMST) based on q-dependent detrended cross-correlation coefficients (ρq). result The qMST graphs provide more information about correlation structure than conventional MSTs. The paper introduces new geometric methods to analyze radar electromagnetic wave statistics.
problem Analyzing spatio-temporal and polarimetric fluctuations of radar electromagnetic waves.
method Using statistical mechanics and Information Geometry, the paper defines a Fréchet barycentre and maximum entropy density for radar measurements.
result New tools for describing radar electromagnetic wave fluctuations, including a distance on covariance matrices.
A bridge between continuous signals and discrete Ising spins for associative memory.
problem Associative memory in continuous-signal-driven Ising spin systems.
method Multilayer Ising framework with PCA whitening and SimHash projection, coupled to pseudo-inverse memory couplings.
result Finite-size scaling of operational storage capacity with αc(N)=αc(∞)−cN−1/2, approaching αc(∞)≈0.50. Paper proposes a method to locate power grid recordings using ENF sequences.
problem Locating power grid recordings without concurrent power signals.
method Extract ENF sequences from power and audio recordings, develop multi-class SVM model.
result Validation of location authenticity of recordings using ENF sequences.
Develops machine learning classifiers for better centrality estimation in proton-nucleus and nucleus-nucleus collisions.
problem Direct measurement of centrality in A-A and p-A collisions is challenging due to limited data access.
method Uses machine learning techniques to classify centrality based on information from multiple detector subsystems.
result Improved centrality resolution can reduce volume fluctuations impact on physical observables.
Study shows cryptocurrency price fluctuations become more similar to national currencies over time.
problem Understanding the volatility and inequality in cryptocurrency prices.
method Calculated inequality measures (Gini, Kolkata indices, Q factor) for cryptocurrency and national currency price fluctuations over 10 years. result Cryptocurrency price fluctuations become more similar to national currencies over time.
Bayesian models' singular fluctuation is shown to be akin to specific heat, influencing model complexity and generalization.
problem Understanding the thermodynamic interpretation of singular fluctuation in Bayesian models.
method Showed singular fluctuation as the curvature of Bayesian free energy and variance of log-likelihood observable under a Gibbs posterior.
result Singular fluctuation is the statistical analogue of specific heat, controlling model complexity and generalization.
We study quantitatively the level of false multifractal signal one may encounter while analyzing multifractal phenomena in time series within multifractal detrended fluctuation analysis (MF-DFA). The investigated effect appears as a result of finite length of used data series and is additionally amplified by the long-t…
We study the nature of fluctuations in variety of price indices involving companies listed on the New York Stock Exchange. The fluctuations at multiple scales are extracted through the use of wavelets belonging to Daubechies basis. The fact that these basis sets satisfy vanishing moments conditions makes them ideal to …
New spectral functionals for Dirac operators with inner fluctuations computed.
problem Spectral functionals and Dirac operators with inner fluctuations.
method Extension of spectral functionals for Dirac operators with inner fluctuations.
result Computed spectral Einstein functional for Dirac operator with inner fluctuations on even-dimensional spin manifolds.
New algorithm for multi-player bandits in changing environments.
problem Sequential action selection with collisions and adversarial losses.
method First provable Multi-player Bandit algorithm for changing environments.
result Resolves open problem in adversarial multi-player settings.
We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of time variable, whereas the physical time is treated as the analog…
The average economic agent is often used to model the dynamics of simple markets, based on the assumption that the dynamics of many agents can be averaged over in time and space. A popular idea that is based on this seemingly intuitive notion is to dampen electric power fluctuations from fluctuating sources (as e.g. wi…
This work studies fluctuation in multilayer neural networks using mean field theory.
problem Understanding fluctuation in multilayer neural networks with mean field training.
method Developed a second-order mean field limit to capture fluctuation, demonstrating stability of gradient descent training.
result Gradient descent training in multilayer networks biases towards minimal fluctuation, even after convergence.
Derives fluctuation theorems and thermodynamic uncertainty relations for systems modeled as Bayes nets.
problem Entropy production in interacting systems modeled as Bayes nets.
method Derives fluctuation theorems and thermodynamic uncertainty relations for arbitrary sets and conditioned sets of systems in Bayes nets.
result Relates the entropy production of the overall system to the precisions of probability currents in individual systems.