کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4944279 | 1437986 | 2017 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hidden Markov model with dependence jumps for predictive modeling of multidimensional time-series
ترجمه فارسی عنوان
یک مدل مارکف مخفی با جهش وابستگی برای مدل سازی پیش بینی شده سری زمانی چند بعدی
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کلمات کلیدی
دینامیک موقتی، مدل های مخفی مارکوف، انتظار برای به حداکثر رساندن، سفارش متغیر جهش وابستگی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volumes 412â413, October 2017, Pages 50-66
Journal: Information Sciences - Volumes 412â413, October 2017, Pages 50-66
نویسندگان
Anastasios Petropoulos, Sotirios P. Chatzis, Stelios Xanthopoulos,