Found 44 papers
Sorted by: Newest FirstWeight-calibrated estimation for factor models of high-dimensional time series*
Qiwei Yao, Bo Zhang, Xinghao Qiao et al.
Scalable Bayesian Inference for Time Series via Divide and Conquer
David Dunson, Rihui Ou, Lachlan Astfalck et al.
Bayesian Spatiotemporal Wombling
Sudipto Banerjee, Didong Li, Aritra Halder
Nonparametric generative modeling for time series via Schrödinger bridge
Mohamed Hamdouche, Pierre Henry-Labordère, Huyên Pham
We propose a novel generative model for time series based on Schrödinger bridge (SB) approach. This consists in the entropic interpolation via optimal...
Statistical Test for Attention in Transformers for Images and Time Series
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka et al.
Transformer models have achieved exceptional performance in various domains, including computer vision and time-series analysis. Their core attention ...
Embedding Network Autoregression for Time Series Analysis and Causal Peer Effect Inference
Jae Ho Chang, Subhadeep Paul
We propose an Embedding Network Autoregressive Model for multivariate networked longitudinal data. We assume the network is generated from a latent va...
FLAGG: Flexible Autoregressive Graph Generation
Samuel Cognolato, Alessandro Sperduti, Luciano Serafini
The Deep Graph Generation's panorama spans two extremes: one-shot and sequential models. The former generates nodes and edges jointly, while the latte...
Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features
Shangtong Zhang, Jiuqi Wang
Temporal difference (TD) learning with linear function approximation (linear TD) is a classic and powerful prediction algorithm in reinforcement learn...
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
S{\'{e}}bastien Lachapelle, Pau Rodr{\'{i}}guez L{\'{o}}pez, Yash Sharma et al.
This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of interes...
Nonparametric generative modeling for time series via Schr{\"{o}}dinger bridge
Mohamed Hamdouche, Pierre Henry-Labord{\`{e}}re, Huy{\^{e}}n Pham
We propose a novel generative model for time series based on Schrödinger bridge (SB) approach. This consists in the entropic interpolation via optimal...
Tail postcoloring in long-run variance estimation of time series
Xu Liu, Kin Wai Chan
Mixture Modeling for Temporal Point Processes with Memory
Bruno Sansó, Xiaotian Zheng, Athanasios Kottas
Asymmetric Penalties Underlie Proper Loss Functions in Probabilistic Forecasting
E Buchweitz, J V Romano, R J Tibshirani
Summary Accurately forecasting the probability distribution of phenomena of interest is a classic and ever more widespread goal in s...
A new class of functional conditional autoregressive models
S Kim
Summary We introduce a new class of conditional autoregressive models for spatially dependent functional data, formulated through co...
Structural classification of locally stationary time series based on second-order characteristics
Xiucai Ding, Lexin Li, Chen Qian
Abstract Time series classification is crucial for numerous scientific and engineering applications. In this article, we present a n...
Stationarity of Manifold Time Series
Dehan Kong, Junhao Zhu, Zhaolei Zhang et al.
A Statistician’s Overview of Physics-Informed Neural Networks for Spatio-Temporal Data
Christopher K. Wikle, Joshua North, Giri Gopalan et al.
Localized Sparse Principal Component Analysis of Multivariate Time Series in the Frequency Domain
Amita Manatunga, Jamshid Namdari, Fabio Ferrarelli et al.
Testing for integer integration in functional time series
Won-Ki Seo, Han Lin Shang
Inference for structural changes in nonstationary functional time series with partial measurement error
Weichi Wu, Lujia Bai, Qirui Hu
Abstract We study the problem of detecting and localizing change points for a general class of locally stationary functional time se...
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
Luca Menicali, Andrew Grace, David H. Richter et al.
Autoregressive networks with dependent edges
Qiwei Yao, Jinyuan Chang, Qin Fang et al.
Abstract We propose an autoregressive framework for modelling dynamic networks with dependent edges. It encompasses models that acco...
Tail-robust factor modelling of vector and tensor time series in high dimensions
Haeran Cho, Matteo Barigozzi, Hyeyoung Maeng
Summary We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, w...
Generalized Multivariate Threshold Autoregressive Models with Linearly Partitioned Threshold Space
Gan Yuan, Chun Yip Yau
Beyond the mean: limit theory and tests for infinite-mean autoregressive conditional durations
Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek et al.
Abstract Integrated autoregressive conditional duration (ACD) models serve as counterparts to integrated generalized autoregressive ...
Structural Identification for Spatio-Temporal Dynamic Models
Cong Cheng, Yuan Ke, Wenyang Zhang et al.
A factor-copula latent-vine time series model for extreme flood insurance losses
Xiaoting Li, Harry Joe, Christian Genest
Efficient Online Prediction for High-Dimensional Time Series via Joint Tensor Tucker Decomposition
Defeng Sun, Zhenting Luan, Haoning Wang et al.
Real-time prediction plays a vital role in various control systems, such as traffic congestion control and wireless channel resource allocation. In th...
Identification and estimation for matrix time series CP-factor models
Qiwei Yao, Jinyuan Chang, Yue Du et al.
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Shiwei Lan, Mirjeta Pasha, Shuyi Li et al.
Spectral change point estimation for high-dimensional time series by sparse tensor decompositionGet access
Xinyu ZhangandKung-Sik Chan
Estimation of Grouped Time-Varying Network Vector Autoregressive Models
Degui Li, Bin Peng, Songqiao Tang et al.
On Non-asymptotic Theory of Recurrent Neural Networks in Temporal Point Processes
Zhiheng Chen, Guanhua Fang, Wen Yu
Temporal point process (TPP) is an important tool for modeling and predicting irregularly timed events across various domains. Recently, the recurrent...
Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models
Sudipto Banerjee, Xiang Chen, Ian Frankenburg et al.
We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the me...
Simultaneous inference for monotone and smoothly time-varying functions under complex temporal dynamics
Tianpai Luo, Weichi Wu
Optimal Vintage Factor Analysis with Deflation Varimax
Xin Bing, Xin He, Dian Jin et al.
Identifying the Structure of High-Dimensional Time Series via Eigen-Analysis
Bo Zhang, Jiti Gao, Guangming Pan et al.
Design and analysis of randomized trials to estimate spatio-temporally heterogeneous treatment effects
Samuel I. Watson, Thomas A. Smith
Frequency Domain Statistical Inference for High-Dimensional Time Series
Jonas Krampe, Efstathios Paparoditis
Nonparametric data segmentation in multivariate time series via joint characteristic functions
E T McGonigle, H Cho
Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in ...
Discovering the Network Granger Causality in Large Vector Autoregressive Models
Yoshimasa Uematsu, Takashi Yamagata
High-Dimensional Knockoffs Inference for Time Series Data
Yingying Fan, Jinchi Lv, Chien-Ming Chi et al.
On the Modeling and Prediction of High-Dimensional Functional Time Series
Qiwei Yao, Jinyuan Chang, Xinghao Qiao et al.