arXiv research

A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.

Study analyzes Echo State Network parameters for Rossler attractor dynamics.

This study uses persistent homology to analyze complex transitional networks from time series data.

Empirical mode modeling improves state-space analysis of noisy data.

The paper explores states of financial markets using correlation matrices and their dynamics.

Improved analysis of UCRL2 with empirical Bernstein inequality reduces exploration-exploitation regret.

Structured state space models improve ECG classification and reveal new insights.

Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change-point algorithm is used to identify the time indices (change points) at which the system transitions …

A quantitative analysis of the basic components of the daily DJIA. The parameters of the underlying Lorentzian states are obtained by fitting the data. Statistical properties of the states are discussed. This is a practical development of the general method introduced in arXiv:1203.6021.

Meta-analysis finds people value insurance for low-probability risks more than expected.

Proposes a flexible neural model for multi-state survival analysis.

Study analyzes profitability and efficiency of Chinese banks, finding state-owned banks superior.

A new framework for time series analysis using state-space learning.

New method shows hidden state can significantly improve differential privacy in SGD.

Study improves ECG analysis accuracy using state space models, self-supervised learning, and patient metadata.

This paper provides an analysis of the tradeoff between asymptotic bias (suboptimality with unlimited data) and overfitting (additional suboptimality due to limited data) in the context of reinforcement learning with partial observability. Our theoretical analysis formally characterizes that while potentially increasin…

Kernel testing compares cell states in single-cell data.

This paper analyzes discrete diffusion models, deriving convergence bounds for their generated samples.

An ADRC-incorporated SGD algorithm improves latent factor analysis speed and accuracy.

Deep learning improves chaotic dynamics filtering without ensemble.

Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes. Here we analyse the temporal and spatial dynamics of these states, as measured by EEG, with a hidden Markov model and compare this approach to classical EEG mic…

Paper classifies economic states and optimizes portfolios for stagflationary environments.

Global stability bounds for matrix frames in phase retrieval problems.

Clusters of financial market states identified over 2006-2019.

Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations a…

Deep learning models detect nanopore translocation events with high accuracy.

A typical audio signal processing pipeline includes multiple disjoint analysis stages, including calculation of a time-frequency representation followed by spectrogram-based feature analysis. We show how time-frequency analysis and nonnegative matrix factorisation can be jointly formulated as a spectral mixture Gaussia…

Interpretable survival analysis improves heart failure risk prediction.

Study analyzes data breach reporting patterns and frequency across U.S. states, finding increasing trends after 2020.

Novel framework proves fast RL convergence in continuous spaces.

Study predicts synchronization state of financial time series using cross-recurrence plots.

A new RL paradigm reduces state-action-value function approximation inefficiency.

Improved analysis of UCBVI algorithm with better empirical performance.

Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While these networks are increasingly applied to nontrivial forecasting tasks, there is a …

We employ unsupervised machine learning techniques to learn latent parameters which best describe states of the two-dimensional Ising model and the three-dimensional XY model. These methods range from principal component analysis to artificial neural network based variational autoencoders. The states are sampled using …

Research shows SBP's tone impacts stock market returns positively or negatively.

DeepGG generates graph distributions for drug discovery and molecular design.

Lyapunov's second theorem is an essential tool for stability analysis of differential equations. The paper provides an analog theorem for incremental stability analysis by lifting the Lyapunov function to the tangent bundle. The Lyapunov function endows the state-space with a Finsler structure. Incremental stability is…

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting…

Price movements of stock market are not totally random. In fact, what drives the financial market and what pattern financial time series follows have long been the interest that attracts economists, mathematicians and most recently computer scientists [17]. This paper gives an idea about the trend analysis of stock mar…

A new method for state estimation in state-space models using incomplete data.

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality o…

Model predicts epileptic seizures by detecting preictal state using wavelet transform and PCA.

The paper introduces a method for interpretable principal component analysis of high-dimensional time series.

Nowadays, when crashes and crises are rather frequent events, an effective monitoring system for the international financial market is needed. Modern nonlinear methods, such as Recurrence Quantification Analysis (RQA), demonstrate the ability to reveal the regularities of the system behavior. Thus, they can be useful f…

Efficiently sparsifies simplicial complexes using local densities of states.

This paper investigates to identify the requirement and the development of machine learning-based mobile big data analysis through discussing the insights of challenges in the mobile big data (MBD). Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the …

We extend a recent synchronization analysis of exact finite-state sources to nonexact sources for which synchronization occurs only asymptotically. Although the proof methods are quite different, the primary results remain the same. We find that an observer's average uncertainty in the source state vanishes exponential…