Search results for "Time series analysis"
showing 10 items of 53 documents
Inferring causation from time series in earth system sciences
2019
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
A new approach to portfolio selection based on forecasting
2023
In this paper we analyze the portfolio selection problem from a novel perspective based on the analysis and prediction of the time series corresponding to the portfolio’s value. Namely, we define the value of a particular portfolio at the time of its acquisition. Using the time series of historical prices of the different financial assets, we calculate backward the value that said portfolio would have had in past time periods. A damped trend model is then used to analyze this time series and to predict the future values of the portfolio, providing estimates of the mean and variance for different forecasting horizons. These measures are used to formulate the portfolio selection problem, whic…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
2021
Many complex systems in physics, biology and engineering are modeled as dynamical networks and described using multivariate time series analysis. Recent developments have shown that the emergent dynamics of a network system are significantly affected by interactions involving multiple network nodes which cannot be described using pairwise links. While these higher-order interactions can be probed using information-theoretic measures, a rigorous framework to describe them in the frequency domain is still lacking. This work presents an approach for the spectral decomposition of multivariate information measures, capable of identifying higher-order synergistic and redundant interactions betwee…
A new Framework for the Spectral Information Decomposition of Multivariate Gaussian Processes
2021
: Different information-theoretic measures are available in the literature for the study of pairwise and higher-order interactions in multivariate dynamical systems. While these measures operate in the time domain, several physiological and non-physiological systems exhibit a rich oscillatory content that is typically analyzed in the frequency domain through spectral and cross-spectral approaches. For Gaussian systems, the relation between information and spectral measures has been established considering coupling and causality measures, but not for higher-order interactions. To fill this gap, in this work we introduce an information-theoretic framework in the frequency domain to quantify t…
Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview
2021
Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of …
Statistical geometric affinity in human brain electric activity
2007
10 pages, 9 figures.-- PACS nrs.: 87.19.La; 05.45.Tp.-- ISI Article Identifier: 000246890100105
Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing
2019
The development of connected health technologies for the continuous monitoring of the psychophysical state of individuals performing daily life activities requires the aggregation of non-intrusive sensors and the availability of methods and algorithms for extracting the relevant physiological information. The present study proposes an integrated approach for the objective assessment of mental stress which combines wirelessly connected low invasive biosensors with multivariate physiological time series analysis. In a group of 18 healthy subjects monitored in a relaxed resting state and during two experimental conditions inducing mental stress and sustained attention (respectively, mental ari…
An Online Time Warping based Map Matching for Vulnerable Road Users’ Safety
2018
International audience; High penetration rate of Smartphones and their increased capabilities to sense, compute, store and communicate have made the devices vital components of intelligent transportation systems. However, their GPS positions accuracy remains insufficient for a lot of location-based applications especially traffic safety ones. In this paper, we developed a new algorithm which is able to improve smartphones GPS accuracy for vulnerable road users' traffic safety. It is a two-stage algorithm: in the first stage GPS readings obtained from smartphones are passed through Kalman filter to smooth deviated reading. Then an adaptive online time warping based map matching is applied to…
Higher education impact on human development : A case study from Pakistan
2017
Master's thesis Development management UT503 - University of Agder 2017 Higher education is considered as an essential part of the human development process of the country. In this context, the objective of this study is to explore the returns of higher education on human development indicators and as well as examine the impact of human development on higher education in Pakistan from the period of the 1984 to 2014. For estimation, correlation analysis and regression analysis has been used to investigate the association between Variables. The main purpose of the study is to identify the link between higher education and the three most important human development indicators, such as GDP, emp…