Modeling Unevenly Spaced Multivariate Time Series with Mixed VariableTypes
Abstract
In the last decade, the increasing use of temporal data, especially time series data, has initiated a great deal of research and development attempts. Many data sources in different fields, such as in medicine, education and finance, naturally generate time series data (e.g. electrocardiogram (ECG), weekly sales totals, and prices of stocks) and modeling of this data has become important to understand the system generating the time series. Modeling time series data involves various tasks such as forecasting, discovery of underlying driving factors or variables, or understanding the dynamics of the series in terms of its variance and other characteristics. A fundamental problem in time series analysis approaches is how to represent/model the time series data. However modeling the time series data is difficult when there are hundreds (to thousands) or more variables with thousands (to millions) of observations. And they are of different type (e.g. numerical, ordinal andnominal). For multivariate data, the observations are sampled in nonuniform time points and/or the sampling frequency for each variable may not be the same. Moreover, the interaction between the variables are to be detected, and redundant variables are expected to be linked in many time series modeling problems. In addition to these, there is an uncertainty component to be modeled. Outliers and missing values are common. The temporal and/or spatial effects, and transient effects are important. However, modeling the data under these challenges is problematic. The research focus will be on the development of algorithms for modeling of the time series under aforementioned critical challenges. The approach will benefit from tree-based ensemblelearning strategies to handle a number of statistical learning tasks directly. An open source tool will be delivered as an R package to enable the reproducibility of the results from the proposed components and to facilitate the future research.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2017
- Source ID
- FA95501710138
Entities
People
- Mustafa Baydogan
Organizations
- Air Force Office of Scientific Research
- Boğaziçi University
- United States Air Force