scholarly journals Multiple Time Series Data Processing for Classification with Period Merging Algorithm

2014 ◽  
Vol 37 ◽  
pp. 301-308 ◽  
Author(s):  
Wei-Ti Su ◽  
Xiao-Ou Ping ◽  
Yi-Ju Tseng ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Merging Algorithm

scholarly journals A framework to analyze multiple time series data: A case study with Streptomyces coelicolor

2005 ◽  
Vol 33 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Sarika Mehra ◽  
Wei Lian ◽  
Karthik P. Jayapal ◽  
Salim P. Charaniya ◽  
David H. Sherman ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Streptomyces Coelicolor ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series

scholarly journals Causal inference with multiple time series: principles and problems

2013 ◽  
Vol 371 (1997) ◽  
pp. 20110613 ◽  
Author(s):  
Michael Eichler
Keyword(s):  
Time Series ◽  
Causal Inference ◽  
Granger Causality ◽  
Latent Variables ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Identification Algorithm ◽  
Multiple Time Series ◽  
Theoretical Justification

I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

scholarly journals Edition and Imputation of Multiple Time Series Data Generated by Repetitive Surveys

2021 ◽  
Vol 8 (4) ◽  
pp. 555-577
Author(s):  
Victor M. Guerrero ◽  
Blanca I. Gaspar
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series

scholarly journals Construction of Complex Network with Multiple Time Series Relevance

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 202
Author(s):  
Zongwen Huang ◽  
Lingyu Xu ◽  
Lei Wang ◽  
Gaowei Zhang ◽  
Yaya Liu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Weather Conditions ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Optimal Network ◽  
Multiple Variables ◽  
Threshold Network

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method.

scholarly journals Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

2008 ◽  
Vol 24 (10) ◽  
pp. 1286-1292 ◽  
Author(s):  
Jongrae Kim ◽  
Declan G. Bates ◽  
Ian Postlethwaite ◽  
Pat Heslop-Harrison ◽  
Kwang-Hyun Cho
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Time Series Data ◽  
Linear Time ◽  
Series Data ◽  
Multiple Time ◽  
Time Varying ◽  
Multiple Time Series ◽  
Linear Interactions ◽  
Time Varying Models
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