Inference of a genetic regulatory network model from limited time series data

2011 ◽  
Author(s):  
Saad Haider ◽  
Ranadip Pal
Keyword(s):  
Time Series ◽  
Network Model ◽  
Regulatory Network ◽  
Time Series Data ◽  
Genetic Regulatory Network ◽  
Series Data ◽  
Limited Time

Inferring Gene Regulatory Network Models from Time-Series Data Using Metaheuristics

2020 ◽  
Author(s):  
Jose Eduardo H. da Silva ◽  
Heder S. Betnardino ◽  
Helio J.C. Barbosa ◽  
Alex B. Vieira ◽  
Luciana C.D. Campos ◽  
...  
Keyword(s):  
Time Series ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Time Series Data ◽  
Network Models ◽  
Series Data ◽  
Gene Regulatory

A Simultaneous Equation Regression Study of Advertising and Sales of Cigarettes

1969 ◽  
Vol 6 (3) ◽  
pp. 291-300 ◽  
Author(s):  
Frank M. Bass
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Simultaneous Equation ◽  
Series Data ◽  
Limited Time ◽  
Regression Methods

This article demonstrates the application of simultaneous equation regression methods in analyzing limited time series data for sales and advertising. In testing a model with sales and advertising relationships for filter and non-filter cigarette brands, we could not reject a model in which the advertising elasticity for filter brands is substantially greater than that for nonfilter brands.

scholarly journals A Cycle Deep Belief Network Model for Multivariate Time Series Classification

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Shuqin Wang ◽  
Gang Hua ◽  
Guosheng Hao ◽  
Chunli Xie
Keyword(s):  
Time Series ◽  
Network Model ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Deep Belief Network ◽  
Learning Ability ◽  
Series Data ◽  
Temporal Data ◽  
Belief Network ◽  
Data Objects

Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.

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