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Title: A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series
Authors: Petropoulos, Anastasios 
Chatzis, Sotirios P. 
Xanthopoulos, Stelios 
Keywords: Dependence jumps;Variable order;Expectation-maximization;Hidden Markov models;Temporal dynamics
Category: Computer and Information Sciences
Field: Natural Sciences
Issue Date: 1-Oct-2017
Source: INFORMATION SCIENCES, Volume: 412, Pages: 50-66, Published: OCT 2017
Journal: Information Sciences
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.
ISSN: 0020-0255
Rights: (C) 2017 Elsevier Inc. All rights reserved.
Type: Article
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