Recent Advances in Time Series Analysis: Methods, Theory, and Applications
This special issue belongs to the section "D1: Probability and Statistics".
Special Issue Information
Dear Colleagues,
Time series analysis plays a key role in understanding the complex dynamics of the data. Demand for such tools arises in a wide range of scientific fields such as finance, engineering, climate, neuroscience, healthcare, etc., which require researchers to infer the trend, uncover hidden patterns, and make informed decisions. Modern applications have imposed contemporary challenges in time analysis, where the data have complex structures and can be high-dimensional, high-volume, non-stationary, non-linear, and non-Gaussian.
This Special Issue will spotlight the recent developments in time series analysis. We cordially invite contributions from the perspectives of methodologies, theories, and applications.
Dr. Mengyu Xu
Guest Editor
Manuscript Submission Information
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Keywords
- time series analysis
- statistics for time series analysis
- forecasting
- trend analysis
- high-dimensional time series
- change point
- application of time series analysis
- empirical analysis in economic, finance, engineering, healthcare, and neuroscience
- hypothesis test
- machine learning methods for time series analysis
- time-varying network
- causality
- spatial time series
- dynamic models
- non-linear time series
- heteroskedasticity
- prediction
- resampling
- ARMA models
- VAR models
- factor models
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Published Papers
Mathematics - ISSN 2227-7390