Oct 1, 2023 · This article novelly proposes a probabilistic multivariate time series anomaly detection framework that unifies data imputation and unsupervised learning. A deep probabilistic graphical model abbreviated SCNF is first devised for unsupervised density estimation.

In this paper, we present an algorithm to learn the structure from data for directed graphical models for stationary Gaussian time series. We describe an algorithm for efficient forecasting for stationary Gaussian time series whose spectral densities factorize in a graphical model.

Feb 15, 2011 · We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependences.

Apr 16, 2018 · In this paper, we aim to provide a methodological introduction to a powerful probabilistic network model applicable in exploratory data analysis, the Gaussian graphical model (GGM), and to propose how it can be used and interpreted in the analysis of both cross-sectional and time-series data. Two lines of network research in psychology.

Apr 25, 2021 · Probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and expressing uncertainty about unknowns, but it lacks flexibility. Deep learning (DL) is an alternative framework for learning from data that has achieved great empirical success in recent years.

Many time-series studies are made to the determination of the significant variables to be considered to get economies. The fundamental issue is to make a model closefisted for example with...

By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data.

Jul 18, 2024 · To mitigate such a dilemma, we present a unified Probabilistic Graphical Model to Jointly capturing intra-/inter-series correlations and modeling the time-variant transitional distribution, and instantiate a neural framework called JointPGM for …

Probabilistic graphical models use a graph-based representation as the ba-sis for compactly encoding a complex distribution over a high-dimensional space. In this graphical representation the nodes correspond to the variables in our domain, and the edges correspond to direct probabilistic interaction between them.

If it matters, I know the distributions $x(y_1),\cdots,x(y_n)$, and that there are only six possible hidden states, and have lots of annotated data to train the model on. Were it that simple, I think a hidden Markov model and the Viterbi algorithm would do the trick.