Abstract
This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.
Highlights
Modeling and decision-making in complex dynamical systems often rely on time series collected from heterogeneous sources
Fast and accurate prediction of the system states and estimation of the associated parameters is essential for online monitoring and active control of the dynamical system; for example, real-time prediction of future states can significantly improve active control of thermoacoustic instabilities [4]
The goal of this paper is to develop a flexible and parsimonious model of categorical time series in a Bayesian nonparametric setting for fusion of correlated information from heterogeneous sources, which can be used for sequential classification and causal inference
Summary
Modeling and decision-making in complex dynamical systems (e.g., distributed physical processes [1], macro-economy [2] and human brain [3]) often rely on time series collected from heterogeneous sources. The goal of this paper is to develop a flexible and parsimonious model of categorical time series in a Bayesian nonparametric setting for fusion of correlated information from heterogeneous sources (e.g., sensors of possibly different modalities), which can be used for sequential classification and causal inference. From this perspective, major contributions of the paper are delineated as follows: 2. By introducing latent variables and sparsity inducing priors, a flexible and parsimonious model is developed for fusion of correlated information from heterogeneous sources (e.g., sensors of possibly different modalities), which can be used to improve the performance of sequential classification tasks. The nomenclature and list of acronyms are provided at the end before the list of references
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