Measures of Analysis of Time Series (MATS): A MATLAB Toolkit for Computation of Multiple Measures on Time Series Data Bases

In many applications, such as physiology and finance, large time series data bases are to be analyzed requiring the computation of linear, nonlinear and other measures. Such measures have been developed and implemented in commercial and freeware softwares rather selectively and independently. The Measures of Analysis of Time Series ({\tt MATS}) {\tt MATLAB} toolkit is designed to handle an arbitrary large set of scalar time series and compute a large variety of measures on them, allowing for the specification of varying measure parameters as well. The variety of options with added facilities for visualization of the results support different settings of time series analysis, such as the detection of dynamics changes in long data records, resampling (surrogate or bootstrap) tests for independence and linearity with various test statistics, and discrimination power of different measures and for different combinations of their parameters. The basic features of {\tt MATS} are presented and the implemented measures are briefly described. The usefulness of {\tt MATS} is illustrated on some empirical examples along with screenshots.Comment: 25 pages, 9 figures, two tables, the software can be downloaded at http://eeganalysis.web.auth.gr/indexen.ht

A conceptual design tool: Sketch and fuzzy logic based system

A real time sketch and fuzzy logic based prototype system for conceptual design has been developed. This system comprises four phases. In the first one, the system accepts the input of on-line free-hand sketches, and segments them into meaningful parts by using fuzzy knowledge to detect corners and inflection points on the sketched curves. The fuzzy knowledge is applied to capture user’s drawing intention in terms of sketching position, direction, speed and acceleration. During the second phase, each segmented sub-part (curve) can be classified and identified as one of the following 2D primitives: straight lines, circles, circular arcs, ellipses, elliptical arcs or B-spline curves. Then, 2D topology information (connectivity, unitary constraints and pairwise constraints) is extracted dynamically from the identified 2D primitives. From the extracted information, a more accurate 2D geometry can be built up by a 2D geometric constraint solver. The 2D topology and geometry information is then employed to further interpretation of a 3D geometry. The system can not only accept sketched input, but also users’ interactive input of 2D and 3D primitives. This makes it friendly and easier to use, in comparison with ‘sketched input only’, or ‘interactive input only’ systems. Finally, examples are given to illustrate the system

A Time Series Analysis: Exploring the Link between Human Activity and Blood Glucose Fluctuation

In this thesis, time series models are developed to explore the correlates of blood glucose (BG) fluctuation of diabetic patients. In particular, it is investigated whether certain human activities and lifestyle events (e.g. food and medication consumption, physical activity, travel and social interaction) influence BG, and if so, how. A unique dataset is utilized consisting of 40 diabetic patients who participated in a 3-day study involving continuous monitoring of blood glucose (BG) at five minute intervals, combined with measures for sugar; carbohydrate; calorie and insulin intake; physical activity; distance from home; time spent traveling via public transit and private automobile; and time spent with other people, dining and shopping. Using a dynamic regression model fitted with autoregressive integrated moving average (ARIMA) components, the influence of independent predictive variables on BG levels is quantified, while at the same time the impact of unknown factors is defined by an error term. Models were developed for individuals with overall findings demonstrating the potential for continuous monitoring of diabetic (DM) patients who are trying to control their BG. Model results produced significant BG predicting variables that include food consumption, exogenous insulin administration and physical activity

New Non-Linearity Test to Circumvent the Limitation of Volterra Expansion

In this article we propose a quick, efficient, and easy method to detect whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. Our proposed test could also be used to test whether the model, including linear and nonlinear, hypothesized to be used for the variable is appropriate as long as the residuals of the model being used could be estimated. Our simulation results show that our proposed test is stable and powerful while our illustration on Wolf's sunspots numbers is consistent with the findings from existing literature

Simulation Analysis and Meta-Analysis of Single-Case Experimental Designs

Single-case experimental designs remain outside of mainstream methodology despite their substantial contributions to our understanding of human behavior. An obstacle to wider adoption is the lack of consensus regarding the analysis and meta-analysis of single-case data. Many single-case statistical methods have been proposed; nearly all are limited by the incompleteness of their models or their lack of formal statistical development, both limitations that inhibit research synthesis and knowledge building. This dissertation, presented in three manuscripts, introduces a simulation-based method of analysis and meta-analysis for single-case experimental designs. Interrupted Times-Series Simulation (ITSSIM) estimates treatment effect sizes by modeling level, trend, variance, and autocorrelation parameters. Parameter estimates are naturally imprecise in brief time-series. ITSSIM compensates for this imprecision by using an iterative procedure to model many plausible parameter values given the observed data. ITSSIM calculates an effect size by comparing a distribution of plausible “null effects”—the no-treatment predictions based on baseline data—to a distribution of plausible treatment effects. ITSSIM effect size estimates, reported as correlation coefficients, standardized mean differences, or unstandardized effects, are interpretable for both clinical practice and quantitative research synthesis. Three studies provide evidence for the content validity, construct validity, and criterion validity of ITSSIM effect size estimates using theoretical, comparative, and deductive strategies, respectively. The first study establishes the theoretical rationale for single-case simulation methods generally, and ITSSIM specifically. ITSSIM produced effect size estimates comparable to five sophisticated multilevel methods when a study of disruptive classroom behavior was reanalyzed. In the second study, ITSSIM produced mean effect size estimates consistent with similar meta-analyses of group-design research. The third study field tests a new software tool for simulation research of single-case statistics. ITSSIM performed reliably under a variety of simulation conditions, controlling for baseline trend and autocorrelation. The results from these three studies indicate that ITSSIM is a powerful, comprehensive method for analysis and meta-analysis of single-case experimental designs. ITSSIM effect size estimates are consistent with other, previously published single-case statistics, and it yields reasonable results even under extreme simulation conditions. ITSSIM is recommended to single-case investigators who wish to better understand their single-case data and treatment effects