arXiv research

Dynamic factor analysis reveals insights into Philippine stock market dynamics.

Proposes iVDFM for identifying latent factors in multivariate time series.

RVRAE combines deep learning and dynamic factor models for better stock returns prediction.

FOCUS method forecasts counterfactuals in panel data with time series dynamics.

Dynamic risk factor model improves portfolio performance in high dimensions.

Improves scalability and robustness of dynamic graph clustering.

dCMF models evolving patterns in multiway data with temporal dynamics.

AlphaForge mines and dynamically combines alpha factors for better investment performance.

Modeling dynamic user interests using neural matrix factorization.

Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data should be analyzed. Here we introduce LFADS (Latent Factor Analysis via Dynamical Systems), a method to infer latent dynamics from simultaneous…

Study on liquidity dynamics in Uniswap v3 pools using statistical methods.

Develops polynomial diffusion models for multi-factor commodity futures dynamics.

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an ex…

Dynamical-VAE learns causal dynamics from POMDPs using future information.

Proposes a model to handle mobile health data with irregular measurements.

MoNODEs improve neural ODEs by separating dynamic states from static factors.

We propose the factorized action variational autoencoder (FAVAE), a state-of-the-art generative model for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and tr…

The article uses dynamic factor allocation to improve portfolio performance by integrating regime-switching signals.

Method ranks generative models without needing latent factor supervision.

Model dynamic customer sensitivities across categories.

Improves predictions by integrating forward-looking views into dynamic factor models.

Model for dynamic relational data with regime changes.

Novel method for estimating currency option parameters with improved accuracy.

Dynamic model captures spatial, temporal, and spatiotemporal volatility effects.

A new framework models and simulates multibody systems using factor graphs.

Novel method models dynamic brain graphs from time series data.

The paper solves multi-period portfolio selection with constraints using a dynamic factor model.

Optimizes neural network training by dynamically updating Tucker decomposition ranks.

Bayesian method for dynamic correlation matrices improves accuracy and responsiveness.

DMSTF models spatio-temporal data with deep Markov priors.

Paper proposes SDDP for improving time series forecasting with high-dimensional predictors.

Unified framework combines views and optimization for better portfolio management.

Develops a dynamic latent-factor model for high-dimensional asset characteristics.

The paper studies the continuous-time dynamics of VIX with stochastic volatility and jumps in VIX and volatility. Built on the general parametric affine model with stochastic volatility and jump in logarithm of VIX, we derive a linear relation between the stochastic volatility factor and VVIX index. We detect the exist…

New method predicts dynamic relationships in terrorist networks.

Proposes a method to learn policies from offline data with reduced bias.

New model analyzes dynamic correlations in stock returns.

Introduces LTQL for factored policies in cooperative MARL.

Generative adversarial networks (GANs) are notoriously difficult to train and the reasons underlying their (non-)convergence behaviors are still not completely understood. By first considering a simple yet representative GAN example, we mathematically analyze its local convergence behavior in a non-asymptotic way. Furt…

Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferen…

We show how random matrix theory can be applied to develop new algorithms to extract dynamic factors from macroeconomic time series. In particular, we consider a limit where the number of random variables N and the number of consecutive time measurements T are large but the ratio N / T is fixed. In this regime the unde…

New method models covariates and responses without parametric assumptions using manifold learning.

Time series of graphs are increasingly prevalent in modern data and pose unique challenges to visual exploration and pattern extraction. This paper describes the development and application of matrix factorizations for exploration and time-varying community detection in time-evolving graph sequences. The matrix factori…

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. The result of our factorized graphical model is a well-organized and coherent…

Enhanced AI analysis predicts S&P 500 stock dynamics using various financial metrics.

A new method combines predictors and their lags using supervised PCA for dynamic forecasting.

New method linearizes nonlinear coupled oscillators on graphs.

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be ge…