Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Han Yuefeng"'
Multi-dimensional time series data, such as matrix and tensor-variate time series, are increasingly prevalent in fields such as economics, finance, and climate science. Traditional Transformer models, though adept with sequential data, do not effecti
Externí odkaz:
http://arxiv.org/abs/2410.20439
Tensor classification is gaining importance across fields, yet handling partially observed data remains challenging. In this paper, we introduce a novel approach to tensor classification with incomplete data, framed within high-dimensional tensor lin
Externí odkaz:
http://arxiv.org/abs/2410.14783
Tensor classification has become increasingly crucial in statistics and machine learning, with applications spanning neuroimaging, computer vision, and recommendation systems. However, the high dimensionality of tensors presents significant challenge
Externí odkaz:
http://arxiv.org/abs/2409.14397
Matrix time series, which consist of matrix-valued data observed over time, are prevalent in various fields such as economics, finance, and engineering. Such matrix time series data are often observed in high dimensions. Matrix factor models are empl
Externí odkaz:
http://arxiv.org/abs/2407.05624
High-dimensional tensor-valued data have recently gained attention from researchers in economics and finance. We consider the estimation and inference of high-dimensional tensor factor models, where each dimension of the tensor diverges. Our focus is
Externí odkaz:
http://arxiv.org/abs/2406.17278
This paper studies the prediction task of tensor-on-tensor regression in which both covariates and responses are multi-dimensional arrays (a.k.a., tensors) across time with arbitrary tensor order and data dimension. Existing methods either focused on
Externí odkaz:
http://arxiv.org/abs/2405.19610
Publikováno v:
Shipin Kexue, Vol 45, Iss 4, Pp 232-238 (2024)
The changes in the main active ingredients and sensory quality of Gastrodia elata during liquid-state fermentation with the traditional Kombucha consortium were analyzed in the study. The results showed that the total acid concentration of the fermen
Externí odkaz:
https://doaj.org/article/6877e03b2c694915947232c994faa615
Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form s
Externí odkaz:
http://arxiv.org/abs/2110.15517
Autor:
Han, Yuefeng, Zhang, Cun-Hui
The CP decomposition for high dimensional non-orthogonal spiked tensors is an important problem with broad applications across many disciplines. However, previous works with theoretical guarantee typically assume restrictive incoherence conditions on
Externí odkaz:
http://arxiv.org/abs/2108.04428
We propose a contemporaneous bilinear transformation for a $p\times q$ matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when $p$ and/or $q$ are large. The resulting transformed matrix assumes a block str
Externí odkaz:
http://arxiv.org/abs/2103.09411