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We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
This paper develops a novel method for policy choice in a dynamic setting where the available data is a multivariate time series. Building on the statistical treatment choice framework, we propose Tim...
Inspired by the study of air quality level data, we propose a new model for the normalcy-dominant ordinal time series. The proposed model is based on a new zero-one-inflated bounded Poisson distributi...
Inspired by the study of air quality level data, we propose a new model for the normalcy-dominant ordinal time series. The proposed model is based on a new zero-one-inflated bounded Poisson distributi...
Crop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a c...
Temporal analysis of Landsat 8 images opens up new opportunities in the unmixing procedure. Although spectral analysis of time series Landsat imagery has its own advantage, it has rarely been studied....
Vegetation plays a leading role in ecosystems. Plant communities are the main components of ecosystems. Green plants in ecosystems are the primary producers, and they provide the living organic matter...
The purpose of this workshop is to bring together the leading experts, active scholars and young researchers in Dynamic Network, Spatio-Temporal Processes, and Functional Time Series under the umbrell...
A method is presented for detection of synchronous signals in multidimensional time series data. It is based on estimation of eigenvalues of spectral matrices and canonical coherences in moving time w...

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