Advances in Time Series Analysis in Health
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 54
Special Issue Editor
2. School of Medicine, Deakin University, Geelong 3220, Australia
Interests: machine learning; deep learning; biostatistics; public health; mental health; trauma research
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As health systems generate increasingly large and heterogeneous datasets, robust mathematical frameworks for temporal analysis have become essential for improving surveillance, forecasting, and evaluating policy and intervention programs.
Classical approaches (e.g., state-space modeling, ARIMA, and their variants) have established the theoretical foundations of time series analysis. However, there is a growing body of work integrating deep learning architectures (e.g., Long Short-Term Memory (LSTM) networks and Transformer-based models), alongside multimodal frameworks that fuse diverse data streams such as genomic, clinical, and environmental data.
This special issue brings together contributions spanning both theoretical advances and applied methodologies across a wide range of health-related applications, including administrative electronic health records and high-frequency wearable sensor data. The tension between the interpretability required by clinical and policy audiences and the expressive power of modern machine learning makes mathematical rigor not only valuable but essential.
This issue showcases how sophisticated time series analysis is reshaping our ability to identify shifts in health dynamics and optimize intervention programs.
Dr. Joanna Dipnall
Guest Editor
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- time series analysis
- stochastic processes
- epidemiological modeling
- health data analytics
- forecasting methods
- change-point detection
- high-dimensional time series
- machine learning for time series
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