Innovative Data Science Techniques in High Dimensional Time Series

Abstract

The analysis of high-dimensional time series data presents unique challenges due to the complexity and volume of the data involved. Traditional time series methods often fall short when applied to high-dimensional settings, necessitating the development of new methodologies. This study explores various approaches to address these challenges, focusing on the estimation and inference of high-dimensional time series with Functional Data. Key contributions include the use of SVD algorithm and the presentation of Functional Data Analysis results for high-dimensional dependent data. Additionally, the study discusses the integration of Functional Data Analysis techniques to enhance forecasting accuracy and model selection in high-dimensional contexts. Through a comprehensive review of recent developments and practical applications, this study aims to provide a robust framework for effectively managing and analyzing high-dimensional time series data.