- Author:
- Sergey Kirshner,
- Chair:
- Padhraic Smyth
Vector-valued (or multivariate) time series data commonly occur in various sciences. While modeling univariate time series is well-studied, modeling of multivariate time series, especially finite-valued or categorical, has been relatively unexplored. In this dissertation, we employ hidden Markov models (HMMs) to capture temporal and multivariate dependencies in the multivariate time series data. We modularize the process of building such models by separating the modeling of temporal dependence, multivariate dependence, and non-stationary behavior. We also propose new methods of modeling multivariate dependence for categorical and real-valued data while drawing parallels between these two seemingly different types of data. Since this work is in part motivated by the problem of prediction precipitation over geographic regions from the multiple weather stations, we present in detail models pertinent to this hydrological application and perform a thorough analysis of the models on data collected from a number of different geographic regions.
Cited By
- Fridman L, Reimer B, Mehler B and Freeman W Cognitive Load Estimation in the Wild Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, (1-9)
- Baba A Removing Object Bouncing from Indoor Tracking Data Proceedings of the 2017 ACM Southeast Conference, (134-141)
- Baba A, Jaeger M, Lu H, Pedersen T, Ku W and Xie X Learning-Based Cleansing for Indoor RFID Data Proceedings of the 2016 International Conference on Management of Data, (925-936)
- Chounta I and Avouris N Time series analysis of collaborative activities Proceedings of the 18th international conference on Collaboration and Technology, (145-152)
- Taylor G and Hinton G Products of Hidden Markov Models Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, (522-529)
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