scholarly journals Forecasting the seasonality and trend of pulmonary tuberculosis in Jiangsu Province of China using advanced statistical time-series analyses

2019 ◽  
Vol Volume 12 ◽  
pp. 2311-2322 ◽  
Author(s):  
Qiao Liu ◽  
Zhongqi Li ◽  
Ye Ji ◽  
Leonardo Martinez ◽  
Zia Ul Haq ◽  
...  
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Jiangsu Province ◽  
Time Series Analyses ◽  
Statistical Time

scholarly journals A Framework for Multivariate Analysis of Land Surface Dynamics and Driving Variables—A Case Study for Indo-Gangetic River Basins

2022 ◽  
Vol 14 (1) ◽  
pp. 197
Author(s):  
Soner Uereyen ◽  
Felix Bachofer ◽  
Claudia Kuenzer
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Multivariate Time Series ◽  
Water Area ◽  
Methodological Framework ◽  
Surface Dynamics ◽  
Snow Cover Area ◽  
Time Series Analyses ◽  
Statistical Time

The analysis of the Earth system and interactions among its spheres is increasingly important to improve the understanding of global environmental change. In this regard, Earth observation (EO) is a valuable tool for monitoring of long term changes over the land surface and its features. Although investigations commonly study environmental change by means of a single EO-based land surface variable, a joint exploitation of multivariate land surface variables covering several spheres is still rarely performed. In this regard, we present a novel methodological framework for both, the automated processing of multisource time series to generate a unified multivariate feature space, as well as the application of statistical time series analysis techniques to quantify land surface change and driving variables. In particular, we unify multivariate time series over the last two decades including vegetation greenness, surface water area, snow cover area, and climatic, as well as hydrological variables. Furthermore, the statistical time series analyses include quantification of trends, changes in seasonality, and evaluation of drivers using the recently proposed causal discovery algorithm Peter and Clark Momentary Conditional Independence (PCMCI). We demonstrate the functionality of our methodological framework using Indo-Gangetic river basins in South Asia as a case study. The time series analyses reveal increasing trends in vegetation greenness being largely dependent on water availability, decreasing trends in snow cover area being mostly negatively coupled to temperature, and trends of surface water area to be spatially heterogeneous and linked to various driving variables. Overall, the obtained results highlight the value and suitability of this methodological framework with respect to global climate change research, enabling multivariate time series preparation, derivation of detailed information on significant trends and seasonality, as well as detection of causal links with minimal user intervention. This study is the first to use multivariate time series including several EO-based variables to analyze land surface dynamics over the last two decades using the causal discovery algorithm PCMCI.

Time‐series analyses of firearm‐related Google searches and U.S. suicide rates 2004–2016

2021 ◽  
Author(s):  
Daniel W. Capron ◽  
Rita Andel ◽  
Martin Voracek ◽  
Benedikt Till ◽  
Thomas Niederkrotenthaler ◽  
...  
Keyword(s):  
Time Series ◽  
Suicide Rates ◽  
Time Series Analyses

scholarly journals Comparative Time Series Analysis of Perceptual Responses to Electroacoustic Music

2012 ◽  
Vol 29 (4) ◽  
pp. 359-375 ◽  
Author(s):  
Freya Bailes ◽  
Roger T. Dean
Keyword(s):  
Time Series ◽  
Sound Intensity ◽  
Electroacoustic Music ◽  
Affective Content ◽  
Musical Expertise ◽  
Perceived Change ◽  
Time Series Analyses ◽  
Time Perceptions ◽  
The Relationship ◽  
Acoustic Measure

this study investigates the relationship between acoustic patterns in contemporary electroacoustic compositions, and listeners' real-time perceptions of their structure and affective content. Thirty-two participants varying in musical expertise (nonmusicians, classical musicians, expert computer musicians) continuously rated the affect (arousal and valence) and structure (change in sound) they perceived in four compositions of approximately three minutes duration. Time series analyses tested the hypotheses that sound intensity influences listener perceptions of structure and arousal, and spectral flatness influences perceptions of structure and valence. Results suggest that intensity strongly influences perceived change in sound, and to a lesser extent listener perceptions of arousal. Spectral flatness measures were only weakly related to listener perceptions, and valence was not strongly shaped by either acoustic measure. Differences in response by composition and musical expertise suggest that, particularly with respect to the perception of valence, individual experience (familiarity and liking), and meaningful sound associations mediate perception.

A distributed computing system for multivariate time series analyses of multichannel neurophysiological data

2006 ◽  
Vol 152 (1-2) ◽  
pp. 190-201 ◽  
Author(s):  
Andy Müller ◽  
Hannes Osterhage ◽  
Robert Sowa ◽  
Ralph G. Andrzejak ◽  
Florian Mormann ◽  
...  
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Multivariate Time Series ◽  
Computing System ◽  
Neurophysiological Data ◽  
Time Series Analyses
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