Multivariate Time Series Analysis with Irregularly Sampled Data
| Title | Multivariate Time Series Analysis with Irregularly Sampled Data |
|---|---|
| Summary | This thesis seeks to advance the field of multivariate time series analysis, specifically targeting datasets with irregular sampling intervals. |
| Keywords | Machine Learning, Multivariate Time Series, Explainable AI |
| TimeFrame | 2023-2024 |
| References | |
| Prerequisites | Statistics, Neural Networks, Programming (Python or Matlab) |
| Author | |
| Supervisor | Awais Ashfaq |
| Level | Master |
| Status | Open |
The research will commence with a comprehensive review of existing methods, encompassing interpolation techniques and traditional time series models. The student will then explore advanced statistical and machine learning approaches, including dynamic Bayesian networks and deep learning architectures, tailored to irregularly sampled multivariate time series.
The methodologies will be rigorously evaluated using synthetic and real-world datasets, emphasizing predictive accuracy and insights extraction. Potential research directions include:
1. Developing a dedicated imputation method for irregularly sampled multivariate time series. 2. Investigating the application of attention networks, or others for capturing temporal dependencies. 3. Applying the proposed methodologies to real-life healthcare challenges like early detection of diseases. 4. Investigating model interpretability for insights into underlying processes.
Additionally, the student is encouraged to propose and explore their own research questions and directions.
Contact: Awais Ashfaq (awais.ashfaq@hh.se)
