Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.Peer reviewe

New insights on hidden Markov models for time series data analysis

The goal of this thesis is to develop novel methods for the analysis of financial data by using hidden Markov models based approaches. The analysis focuses on univariate and multivariate financial time series, modeling interrelationships between financial returns throughout different statistical methods, such as graphical models, quantile and expectile regressions. The dissertation is divided into three chapters, each of them examining different classes of assets returns for a comprehensive risk analysis. The methodologies we propose are illustrated using real-world data and simulation studies

An Adaptive Clustering Algorithm for Gene Expression Time-Series Data Analysis

Studying gene expression through various time intervals of breast cancer survival may provide insights into the recovery of the patients. In this work, we propose a hierarchical clustering method used to separate dissimilar groups of genes in time-series data, which have the furthest distances from the rest of the genes throughout dierent time intervals. The isolated outliers(genes that trend dierently from other genes) can serve as potential biomarkers of breast cancer survivability. We partition the time axis (time points) into bins of length six months starting from 1-6 up to 337-342 month intervals and, for each gene, we average its expression level over all patients who appear in a survival bin. Gene expressions throughout those time points are cubic spline interpolated to create a trending prole for each gene. First, we universally align the gene expression proles to minimize the total area between them. Then, we cluster them using a sliding window approach and hierarchical clustering based on minimum vertical distances. To the best of our knowledge, this work is the rst time-series model that is built on the survival time of patients after the treatment. With this approach, we identied 46 genes (including 24 oncogenes and 18 tumor suppressor genes) as potential biomarkers of breast cancer survivability

Newly Revised Study of Daily Time Series Data Analysis of Market Prices

The purpose of this paper is to clarify the existence of time series characteristics of daily stock prices of securities marketed on organized exchanges. This study differs from previous studies where the focus was on index numbers of daily stock market prices rather than the actual prices of traded securities. Furthermore, this study is important because of the theory of market efficiency and its application to short term forecasting of closing prices of traded securities

Does health sector aid matter? Evidence from time-series data analysis in Ethiopia

Aims: Development assistance for health is an important part of financing health care in developing countries. In spite of the increasing volumes in absolute terms in development assistance for health, there are controversies on their effect on health outcomes. Therefore, this study aims to analyze the effect of development assistance for health on health status in Ethiopia. Methods: Using dynamic time series analytic approach for the period 1978-2013, this paper examines whether development assistance for health has contributed for health status change in Ethiopia. While life expectancy at birth was used as a measure of health status, vector error correction model was used for the analysis. Results: Development assistance for health expenditure (lagged one and two years) had a significant positive effect on life expectancy at birth in Ethiopia. Other things being equal, a 1% increase in per capita development assistance for health leads to 0.026 years improvement in life expectancy at birth (P<0.001) in the immediate year following the period of assistance, and 0.008 years (P=0.025) in the immediate two years following the provision of assistance. Conclusion: This study indicates that, seemingly, development assistance for health has significant favourable effect in improving health status in Ethiopia. The policy implication of this finding is development assistance for the health should continue as an interim means to an end. &nbsp