Search results for "Time serie"
showing 10 items of 261 documents
Stochastic resonance in a metal-oxide memristive device
2021
Abstract The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type. The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driving signal is analyzed through the time series statistics of the resistive switching parameters, the spectral response to a periodic perturbation and the signal-to-noise ratio at the output of the nonlinear system. The stabilized resistive switching and the increased memristance response are revealed in the observed regularities…
Non-stationarity tests and nonlinear trends
1992
This paper stresses the importance of the hypothesis of linearity of the deterministic component imposed by unit root testing procedures most frequently used in empirical literature. We suggest an empirical testing strategy which reduces the risk of reaching false conclusions due to the misspecification of that component and we apply it to the analysis of the nonstationarity exhibited by real GNP in France. We show that it is possible to find someflexible specifications which enable us to reject the unit root null hypothesis otherwise strongly supported in empirical literature. These specifications might be considered as approximations of the true process generating real GNP and might be us…
Deep Learning Models Performance For NDVI Time Series Prediction: A Case Study On North West Tunisia
2020
The main goal of this paper is to analyze the performance of two deep learning models Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) network for non-stationary Normalized Difference Vegetation Index (NDVI) time-series prediction. Both methods have provided good performances in the different time series. The BiLSTM has shown the best agreement with the lowest root mean square error (RMSE) and the highest Pearson correlation coefficient (R) of 0.034 and 0.93, respectively.
High-to-low (Regional) fertility transitions in a peripheral european country: The contribution of exploratory time series analysis
2021
Diachronic variations in demographic rates have frequently reflected social transformations and a (more or less evident) impact of sequential economic downturns. By assessing changes over time in Total Fertility Rate (TFR) at the regional scale in Italy, our study investigates the long-term transition (1952–2019) characteristic of Mediterranean fertility, showing a continuous decline of births since the late 1970s and marked disparities between high- and low-fertility regions along the latitude gradient. Together with a rapid decline in the country TFR, the spatiotemporal evolution of regional fertility in Italy—illustrated through an exploratory time series statistical approach—outlines th…
A time series study on the effects of heat on mortality and evaluation of heterogeneity into European and Eastern-Southern Mediterranean cities: resu…
2013
Background: The Mediterranean region is particularly vulnerable to the effect of summer temperature. Within the CIRCE project this time-series study aims to quantify for the first time the effect of summer temperature in Eastern-Southern Mediterranean cities and compared it with European cities around the Mediterranean basin, evaluating city characteristics that explain between-city heterogeneity. Methods: The city-specific effect of maximum apparent temperature (Tappmax) was assessed by Generalized Estimation Equations, assuming a linear threshold model. Then, city-specific estimates were included in a random effect meta-regression analysis to investigate the effect modification by several…
NOAA-AVHRR Orbital Drift Correction From Solar Zenithal Angle Data
2008
This paper presents a new method for NOAA's (National Ocean and Atmospheric Administration) orbital drift correction. This method is pixel-based, and in opposition with most methods previously developed, does not need explicit knowledge of land cover. This method is applied to AVHRR (Advanced Very High Resolution Radiometer) channel information, and relies only on the additional knowledge of solar zenithal angle (SZA) and acquisition date information. In a first step, anomalies in SZA and channel time series are retrieved, and screened out for anomalous values. Then, the part of the parameter anomaly which is explained by SZA anomaly is removed from the data, to estimate new parameter anoma…
Global land surface phenology trends from GIMMS database
2009
A double logistic function has been used to describe global inventory mapping and monitoring studies (GIMMS) normalized difference vegetation index (NDVI) yearly evolution for the 1981 to 2003 period, in order to estimate land surface phenology parameter. A principal component analysis on the resulting time series indicates that the first components explain 36, 53 and 37% of the variance for the start, end and length of growing season, respectively, and shows generally good spatial homogeneity. Mann-Kendall trend tests have been carried out, and trends were estimated by linear regression. Maps of these trends show a global advance in spring dates of 0.38 days per year, a global delay in aut…
Multiset Kernel CCA for multitemporal image classification
2013
The analysis of multitemporal remote sensing images is becoming an increasingly important problem because of the upcoming scenario of multispectral satellite constellations monitoring our Planet. Algorithms that can analyze such amount of heterogeneous information are necessary. While linear techniques have been extensively deployed, this work considers a kernel method that finds nonlinear correlations between all image sources and the class labels. We introduce in this context the Kernel Canonical Correlation Analysis (KCCA) to exploit the wealth of temporal image information and to handle nonlinear relations in a natural way via kernels. To achieve this goal, we use the generalization of …
Assessing directional interactions among multiple physiological time series: The role of instantaneous causality
2012
This paper deals with the assessment of frequency domain causality in multivariate (MV) time series with significant instantaneous interactions. After providing different causality definitions, we introduce an extended MV autoregressive modeling approach whereby each definition is described in the time domain in terms of the model coefficients, and is quantified in the frequency domain by means of novel measures of directional connectivity. These measures are illustrated in a theoretical example showing how they reduce to known indexes when instantaneous causality is trivial, while they describe peculiar aspects of directional interaction in the presence of instantaneous causality. The appl…
MuTE: a new matlab toolbox for estimating the multivariate transfer entropy in physiological variability series
2014
We present a new time series analysis toolbox, developed in Matlab, for the estimation of the Transfer entropy (TE) between time series taken from a multivariate dataset. The main feature of the toolbox is its fully multivariate implementation, that is made possible by the design of an approach for the non-uniform embedding (NUE) of the observed time series. The toolbox is equipped with parametric (linear) and non-parametric (based on binning or nearest neighbors) entropy estimators. All these estimators, implemented using the NUE approach in comparison with the classical approach based on uniform embedding, are tested on RR interval, systolic pressure and respiration variability series mea…