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10.1371/journal.pone.0143015 http://scihub22266oqcxt.onion/10.1371/journal.pone.0143015 C4646626!4646626!26571115     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26571115&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       PLoS+One 2015 ; 10 (11): ä Nephropedia Template TP {{tp|p=26571115|t=2015. Visibility Graph Based Time Series Analysis |pdf=|usr=}}{{26571115}} gab.com Text Visibility Graph Based Time Series Analysis JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26571115 #FOAvip Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it?s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. Twit Text FOAVip Visibility Graph Based Time Series Analysis ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=26571115 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26571115 #FOAvip English Wikipedia <ref>{{Cite pmid|26571115}}</ref> Visibility Graph Based Time Series Analysis #MMPMID26571115 Stephen M; Gu C; Yang H PLoS One 2015[]; 10 (11): ä PMID26571115show ga Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it?s microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks. äVisibility Graph Based Time Series Analysis (epmc).pdf DeepDyve Pubget Overpricing