Detecting a multigranularity event in an unequal interval time series based on self-adaptive segmenting

Analyzing the temporal behaviors and revealing the hidden rules of objects that produce time series data to detect the events that users are interested in have recently received a large amount of attention. Generally, in various application scenarios and most research works, the equal interval sampling of a time series is a requirement. However, this requirement is difficult to guarantee because of the presence of sampling errors in most situations. In this paper, a multigranularity event detection method for an unequal interval time series, called SSED (self-adaptive segmenting based event detection), is proposed. First, in view of the trend features of a time series, a self-adaptive segmenting algorithm is proposed to divide a time series into unfixed-length segmentations based on the trends. Then, by clustering the segmentations and mapping the clusters to different identical symbols, a symbol sequence is built. Finally, based on unfixed-length segmentations, the multigranularity events in the discrete symbol sequence are detected using a tree structure. The SSED is compared to two previous methods with ten public datasets. In addition, the SSED is applied to the public transport systems in Xiamen, China, using bus-speed time-series data. The experimental results show that the SSED can achieve higher efficiency and accuracy than existing algorithms.

Supplemental Material: Centennial to millennial variability of greenhouse climate across the mid-Cenomanian event

Background of the studied interval, time series analysis of the grayscale of the marls, micropaleontology, and supplemental figures and tables.

Supplemental Material: Centennial to millennial variability of greenhouse climate across the mid-Cenomanian event

Background of the studied interval, time series analysis of the grayscale of the marls, micropaleontology, and supplemental figures and tables.

Main areas of development of public-private partnership in providing cancer care

Objectives to substantiate and define the main directions of the development of public-private cooperation in the provision of specialized oncological care to patients. Material and methods.The research was based on the data of official statistics, scientific and methodological literature presented in the eLibrary, Publons, Scopus, the Cochrane Library databases. Research methods included bibliographic, descriptive and analytical, content analysis method, mathematical and statistical (calculation of relative indicators, mean values, methods of aligning the interval time series). Results. Reducing morbidity and mortality from oncopathology is a state-scale problem that can only be solved comprehensively by attracting private investors when implementing innovative public-private partnership projects in healthcare. One of the modern models of interaction between state and business is the construction of public-private partnerships for new medical infrastructure. Conclusion. The Center for Nuclear Medicine for the treatment of cancer is an innovative project, the implementation of which will help to reduce mortality from malignant neoplasms, increase the availability of specialized care and the quality of life of patients.

ANALYSIS OF THE CORRELATION BETWEEN MUSCLE REACTION AND STRIDE INTERVAL VARIABILITY IN SINGLE-TASK AND DUAL-TASK WALKING

Analysis of leg muscle activation and gait variability during locomotion is an important area of research in physiological and sport sciences. In this paper, we analyzed the coupling between the alterations of leg muscle activation and gait variability in single-task and dual-task walking. Since leg muscle activation in the form of electromyogram (EMG) signals and gait variability in the form of stride interval time series have complex structures, fractal theory and approximate entropy were used to evaluate their correlation at various walking conditions. Sixty subjects walked at their preferred speed for 10 min under the single-task condition and for 90[Formula: see text]s under the cognitive dual-task condition, and we evaluated the variations of the fractal dimension and approximate entropy of EMG signals and stride interval time series. According to the results, dual-task walking caused reductions in the complexity of EMG signals and stride interval time series than single-task walking. This technique can be used to evaluate the correlation between other organs during different locomotion.

Bayesian Model Averaging with Temporal Correlation for Time Series Forecasts

This study extends Bayesian model averaging (BMA) to a form suitable for time series forecasts. BMA is applied to a three-member ensemble for temperature forecasts with a 1-h interval time series at specific stations. The results of such an application typically have a problematic characteristic. BMA weights assigned to ensemble members fluctuate widely within a few hours because BMA optimizations are independent at each lead time, which is incompatible with the spatiotemporal continuity of meteorological phenomena. To ameliorate this issue, a degree of correlation among different lead times is introduced by the extension of latent variables to lead times adjacent to the target lead time for the calculation of BMA weights and variances. This extension approach stabilizes the BMA weights, improving the performance of deterministic and probabilistic forecasts. Also, an investigation of the effects of this extension technique on the shapes of forecasted probability density functions showed that the extension approach offers advantages in bimodal cases. This extension technique may show promise in other applications to improve the performance of forecasts by BMA.

Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients

Using information theoretic measures, relations between heart rhythm, repolarization in the tissue of the heart, and the diastolic interval time series are analyzed. These processes are a fragment of the cardiovascular physiological network. A comparison is made between the results for 84 (42 women) healthy individuals and 65 (45 women) long QT syndrome type 1 (LQTS1) patients. Self-entropy, transfer entropy, and joint transfer entropy are calculated for the three time series and their combinations. The results for self-entropy indicate the well-known result that regularity of heart rhythm for healthy individuals is larger than that of QT interval series. The flow of information depends on the direction with the flow from the heart rhythm to QT dominating. In LQTS1 patients, however, our results indicate that information flow in the opposite direction may occur—a new result. The information flow from the heart rhythm to QT dominates, which verifies the asymmetry seen by Porta et al. in the variable tilt angle experiment. The amount of new information and self-entropy for LQTS1 patients is smaller than that for healthy individuals. However, information transfers from RR to QT and from DI to QT are larger in the case of LQTS1 patients.