Comment on “Factor Models for High-Dimensional Tensor Time Series”

DOI10.1080/01621459.2021.2024214zbMath1506.62371OpenAlexW4205479174MaRDI QIDQ5881067

Daniel Peña

Publication date: 9 March 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2021.2024214

zbMATH Keywords

dimension reductiontrafficunfoldingfactor modelsautocovariance matricesimport-exporteigen-analysiscross-covariance matricesdynamic transport networktensor time series

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Measures of association (correlation, canonical correlation, etc.) (62H20) Multilinear algebra, tensor calculus (15A69)

Cites Work

• Unnamed Item
• Factor modeling for high-dimensional time series: inference for the number of factors
• Factor models for matrix-valued high-dimensional time series
• A robust procedure to build dynamic factor models with cluster structure
• Clustering time series by linear dependency
• Eigenvalue Ratio Test for the Number of Factors
• Statistical Learning for Big Dependent Data
• Forecasting Using Principal Components From a Large Number of Predictors
• Determining the Number of Factors in Approximate Factor Models

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