Search results for "Time serie"
showing 10 items of 261 documents
Ambient carbon monoxide and daily mortality:a global time-series study in 337 cities
2021
Background Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. Methods We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimat…
Information transfer and information modification to identify the structure of cardiovascular and cardiorespiratory networks
2017
To fully elucidate the complex physiological mechanisms underlying the short-term autonomic regulation of heart period (H), systolic and diastolic arterial pressure (S, D) and respiratory (R) variability, the joint dynamics of these variables need to be explored using multivariate time series analysis. This study proposes the utilization of information-theoretic measures to measure causal interactions between nodes of the cardiovascular/cardiorespiratory network and to assess the nature (synergistic or redundant) of these directed interactions. Indexes of information transfer and information modification are extracted from the H, S, D and R series measured from healthy subjects in a resting…
Multiscale Information Decomposition Dissects Control Mechanisms of Heart Rate Variability at Rest and During Physiological Stress.
2019
Heart rate variability (HRV
Longitudinal metagenomics
2018
Culture-independent approaches are revolutionizing biology. Whether in a clinical or in an environmental sample, metagenomics can reveal which microorganisms exist and what they actually do. Metagenomic studies have unveiled more microbial diversity in a few years than traditional microbiology in centuries. From the top branches down to the roots, its discoveries are reshaping the tree of life dramatically. Metagenomics is a powerful tool for the study of microbial communities, but it requires equally powerful methods of analysis. Current challenges in the analysis of metagenomic data include the accurate comparison of samples, the estimation of the uncertainty in the results, and the effec…
Introducing the DYNAMICS framework of moment-to-moment development in achievement motivation
2022
This article introduces a new theoretical and psychometric framework describing moment-to-moment development and inter-dependencies of achievement motivation in terms of the situated expectancy-value theory, by introducing dynamical systems concepts into this line of research. As a first empirical example of a study using this framework, we examined whether task values, costs, and success expectancies measured in a learning situation (time point t) predicted themselves and each other at the next situation (t + 1; 27 min later) within a weekly university lecture. Situational task values, expectancies, and costs were assessed using the experience sampling method in 155 university teacher trai…
Modeling foreign exchange market activity around macroeconomic news: Hawkes-process approach
2015
We present a Hawkes-model approach to the foreign exchange market in which the high-frequency price dynamics is affected by a self-exciting mechanism and an exogenous component, generated by the pre-announced arrival of macroeconomic news. By focusing on time windows around the news announcement, we find that the model is able to capture the increase of trading activity after the news, both when the news has a sizable effect on volatility and when this effect is negligible, either because the news in not important or because the announcement is in line with the forecast by analysts. We extend the model by considering noncausal effects, due to the fact that the existence of the news (but not…
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
2014
Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted) local regression filter (LOESS) and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG), sm…
ICA and stochastic volatility models
2016
We consider multivariate time series where each component series is an unknown linear combination of latent mutually independent stationary time series. Multivariate financial time series have often periods of low volatility followed by periods of high volatility. This kind of time series have typically non-Gaussian stationary distributions, and therefore standard independent component analysis (ICA) tools such as fastICA can be used to extract independent component series even though they do not utilize any information on temporal dependence. In this paper we review some ICA methods used in the context of stochastic volatility models. We also suggest their modifications which use nonlinear…
Wiener-Granger Causality in Network Physiology with Applications to Cardiovascular Control and Neuroscience
2016
Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wiener–Granger causality (WGC) has been one of the most relevant concepts exploited by modern time series analysis. Indeed, in networks formed by multiple components, working according to the notion of segregation and interacting with each other according to the principle of integration, inferring causality has opened a window on the effective connectivity of the network and has linked experimental evidences to functions and mechanisms. This tutorial reviews predictability improvement, information-based and frequency domain methods for inferring WGC among physiological processes from multivariate…
Synergy of Sentinel-1 and Sentinel-2 Time Series for Cloud-Free Vegetation Water Content Mapping with Multi-Output Gaussian Processes
2023
Optical Earth Observation is often limited by weather conditions such as cloudiness. Radar sensors have the potential to overcome these limitations, however, due to the complex radar-surface interaction, the retrieving of crop biophysical variables using this technology remains an open challenge. Aiming to simultaneously benefit from the optical domain background and the all-weather imagery provided by radar systems, we propose a data fusion approach focused on the cross-correlation between radar and optical data streams. To do so, we analyzed several multiple-output Gaussian processes (MOGP) models and their ability to fuse efficiently Sentinel-1 (S1) Radar Vegetation Index (RVI) and Senti…