Search results for " time series"
showing 10 items of 75 documents
Traps and Surprises in Long Time Series. Considerations on Italian Living Standards after Unification
2011
Using Italian time series since 1861, we explore the evolution of living standards of Italian population after the Unification. Furthermore, we investigate the informative capacity of the aforementioned series to discover suitable long-run relationships among the variables to be used for a further modelling. Notwithstanding, the dynamics and the statistical characteristics of series have dramatically changed – both within each time series and among all ones – some interesting results have been drawn on the evolution of Italian living standards.
Changes in Onset of Vegetation Growth on Svalbard, 2000–2020
2022
The global temperature is increasing, and this is affecting the vegetation phenology in many parts of the world. The most prominent changes occur at northern latitudes such as our study area, which is Svalbard, located between 76°30′N and 80°50′N. A cloud-free time series of MODIS-NDVI data was processed. The dataset was interpolated to daily data during the 2000–2020 period with a 231.65 m pixel resolution. The onset of vegetation growth was mapped with a NDVI threshold method which corresponds well with a recent Sentinel-2 NDVI-based mapping of the onset of vegetation growth, which was in turn validated by a network of in-situ phenological data from time lapse cameras. The results show th…
On Independent Component Analysis with Stochastic Volatility Models
2017
Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to…
Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp
2017
Blind source separation (BSS) is a well-known signal processing tool which is used to solve practical data analysis problems in various fields of science. In BSS, we assume that the observed data consists of linear mixtures of latent variables. The mixing system and the distributions of the latent variables are unknown. The aim is to find an estimate of an unmixing matrix which then transforms the observed data back to latent sources. In this paper we present the R packages JADE and BSSasymp. The package JADE offers several BSS methods which are based on joint diagonalization. Package BSSasymp contains functions for computing the asymptotic covariance matrices as well as their data-based es…
Recurrence Plots in Nonlinear Time Series Analysis: Free Software
2002
Recurrence plots are graphical devices specially suited to detect hidden dynamical patterns and nonlinearities in data. However, there are few programs available to apply such a mehodology. This paper reviews one of the best free programs to apply nonlinear time series analysis: Visual Recurrence Analysis (VRA). This program is targeted to recurrence analysis and the so-called Recurrence Quantitative Analysis (RQA, the quantitative counterpart of recurrence plots), although it includes many procedures in a friendly visual environment. Comparisons with alternative programs are performed.
Introducing libeemd: a program package for performing the ensemble empirical mode decomposition
2016
The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the deco…
Evaluating currency crises: the case of the European monetary system
2007
In this paper we examine the nature of currency crises. We ascertain whether the currency crises of the European Monetary System (EMS) were based either on fundamentals, or on self-fulfilling market expectations driven by extrinsic uncertainty. In particular, we extend previous work of Jeanne and Masson (J Int Econ 50:327–350, 2000) regarding the evaluation of currency crisis. We contribute to the existing literature proposing the use of Markov regime-switching with time-varying transition probability model. Our empirical results suggest that the currency crises of the EMS were not due only to market expectations driven by external uncertainty, or ‘sunspots’, but also to fundamental variabl…
A time domain triangle method approach to estimate actual evapotranspiration: Application in a Mediterranean region using MODIS and MSG-SEVIRI produc…
2016
Abstract In this study, spatially distributed estimates of regional actual evapotranspiration (ET) were obtained using a revised procedure of the so called “triangle method” to parameterize the Priestley–Taylor ϕ coefficient. In the procedure herein proposed, named Time-Domain Triangle Method (TDTM), the triangular feature space was parameterized considering pairs of T s –VI values obtained by exploring, for each pixel, only their temporal dynamics. This new method was developed using time series products provided by MODIS and MSG-SEVIRI sensors. Moreover the proposed procedure does not depend on ancillary data, and it is only based on remotely sensed vegetation indices and day–night time l…
Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine
2022
Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SC…
Analysis and modeling of wind directions time series
2013
This work aims at studying some aspects of wind directions in Italy and supplying appropriate models. A comparison is presented between independent mixture and Hidden Markov models, which seem to be appropriate as far as the series we studied.