A Generative Model For Time Series Discretization Based On Multiple Normal Distributions

This paper studies a class of models where full identification is not necessarily assumed. We term such models partially identified. It is argued that partially identified systems are of practical importance since empirical investigators frequently proceed under conditions that are best described as apparent identification. One objective of the paper is to explore the properties of conventional statistical procedures in the context of identification failure. Our analysis concentrates on two major types of partially identified model: the classic simultaneous equations model under rank condition failures; and time series spurious regressions. Both types serve to illustrate the extensions that are needed to conventional asymptotic theory if the theory is to accommodate partially identified systems. In many of the cases studied, the limit distributions fall within the class of compound normal distributions. They are simply represented as covariance matrix or scalar mixtures of normals. This includes time series spurious regressions, where representations in terms of functionals of vector Brownian motion are more conventional in recent research following earlier work by the author.

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Analyzing graph time series using a generative model

2016 23rd International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr.2016.7900149 ◽

2016 ◽

Author(s):

Cheng Ye ◽

Richard C. Wilson ◽

Edwin R. Hancock

Keyword(s):

Time Series ◽

Generative Model

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Time-Elastic Generative Model for Acceleration Time Series in Human Activity Recognition

Sensors ◽

10.3390/s17020319 ◽

2017 ◽

Vol 17 (2) ◽

pp. 319 ◽

Cited By ~ 7

Author(s):

Mario Munoz-Organero ◽

Ramona Ruiz-Blazquez

Keyword(s):

Time Series ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition ◽

Generative Model ◽

Acceleration Time

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Structural network inference from time-series data using a generative model and transfer entropy

Pattern Recognition Letters ◽

10.1016/j.patrec.2019.05.019 ◽

2019 ◽

Vol 125 ◽

pp. 357-363 ◽

Cited By ~ 2

Author(s):

Zhihong Zhang ◽

Genzhou Zhang ◽

Zhonghao Zhang ◽

Guo Chen ◽

Yangbin Zeng ◽

...

Keyword(s):

Time Series ◽

Network Inference ◽

Time Series Data ◽

Generative Model ◽

Transfer Entropy ◽

Series Data ◽

Structural Network

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Novel Study of Model-Based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process

Current Aging Science ◽

10.2174/1874609812666191015140449 ◽

2020 ◽

Vol 13 (2) ◽

pp. 178-187

Author(s):

Farzane Ahmadi ◽

Ali-Reza Abadi ◽

Zahra Bazi ◽

Abolfazl Movafagh

Keyword(s):

Gene Expression ◽

Time Series ◽

Nuclear Antigen ◽

General Mechanism ◽

Normal Distributions ◽

Model Based Clustering ◽

Model Based ◽

Therapeutic Decisions ◽

Time Series Gene Expression ◽

Different Tissues

Background: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. Identification of similar genes between tissues in different ages may also help to discover the general mechanism of aging or to discover more effective therapeutic decisions. Objective: Objective: According to the wide application of model-based clustering techniques, the aim is to evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal Distributions (MMVNDs). Methods: In this study, the expression of aging data from NCBI’s Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differentially expressed between different tissues were selected and then clustered together through two methods. Finally, the biological significance of clusters was evaluated, using their ability to find genes in the cell using Enricher. Results: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria, the cancer development incidence, and the chronic kidney failure, respectively. Conclusion: Results showed that most biological changes of aging between tissues are related to the specific components of immune cells. Also, the application of MMVNDs can increase the ability to find similar genes.

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