New Challenges in Time Series and Statistics
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: 20 May 2025 | Viewed by 861
Special Issue Editor
Interests: high-dimensional time series; high-dimensional significance testing; change-point detection; high-order statistics, nonlinear time series; non-stationary time series; boostrapping and subsampling; time-varying network; random graphs
Special Issue Information
Dear Colleagues,
Time series data are becoming increasingly prevalent across various fields, such as finance, economics, engineering, biology, neuroscience, healthcare, and more. The statistical analysis of time series faces numerous challenges that complicate accurate data interpretation and forecasting. One primary issue is dealing with potentially non-Gaussian, non-linear, non-stationary, or high-dimensional data with complex dependence structures. Analyzing and understanding such complex data is essential because it can provide insights into patterns, trends, and relationships that may not be apparent with simpler dependence structures. Traditional theories and methodologies often prove inadequate for modern time series analysis. These challenges underscore the need for advanced statistical methods and computational tools to effectively analyze, interpret, and predict time series data. It is crucial to develop novel approaches for both time-domain and frequency-domain analysis to address these issues comprehensively.
This Special Issue is dedicated to reflecting recent advances in modern time series analysis from both theoretical and applied perspectives. By convening researchers and practitioners in these fields, we aim to highlight cutting-edge innovations and methodologies that address existing challenges.
Dr. Danna Zhang
Guest Editor
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Keywords
- time series analysis
- dependence structure
- causality
- dynamic models
- non-stationarity
- non-linearity
- high dimensionality
- robust analysis
- prediction
- resampling
- statistical inference
- hypothesis testing
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