Paper Info
Title
A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series.
Abstract
Hidden Markov models (HMMs) are a popular approach for modeling sequential data, typically based on the assumption of a first- or moderate-order Markov chain. However, in many real-world scenarios the modeled data entail temporal dynamics the patterns of which change over time. In this paper, we address this problem by proposing a novel HMM formulation, treating temporal dependencies as latent variables over which inference is performed. Specifically, we introduce a hierarchical graphical model comprising two hidden layers: on the first layer, we postulate a chain of latent observation-emitting states, the temporal dependencies between which may change over time; on the second layer, we postulate a latent first-order Markov chain modeling the evolution of temporal dynamics (dependence jumps) pertaining to the first-layer latent process. As a result of this construction, our method allows for effectively modeling non-homogeneous observed data, where the patterns of the entailed temporal dynamics may change over time. We devise efficient training and inference algorithms for our model, following the expectation-maximization paradigm. We demonstrate the efficacy and usefulness of our approach considering several real-world datasets.
Year
DOI
Venue
2017
10.1016/j.ins.2017.05.038
Information Sciences
Keywords
Field
DocType
Temporal dynamics,Hidden Markov models,Expectation-maximization,Variable order,Dependence jumps
Markov model,Expectation–maximization algorithm,Markov chain,Latent variable,Artificial intelligence,Variable-order Markov model,Graphical model,Hidden Markov model,Machine learning,Mathematics,Hidden semi-Markov model
Journal
Volume
ISSN
Citations 
412
0020-0255
5
PageRank 
References 
Authors
0.40
19
3
Name
Order
Citations
PageRank
Anastasios Petropoulos150.40
Sotirios P. Chatzis2305.94
Stelios Xanthopoulos350.40