Quality of Similarity Rankings in Time Series

2011 ◽  
pp. 422-440 ◽  
Author(s):  
Thomas Bernecker ◽  
Michael E. Houle ◽  
Hans-Peter Kriegel ◽  
Peer Kröger ◽  
Matthias Renz ◽  
...  
Keyword(s):  
Time Series

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

DYNAMICS UNDERLYING PATIENT-VENTILATOR INTERACTIONS DURING NOCTURNAL NONINVASIVE VENTILATION

2012 ◽  
Vol 22 (02) ◽  
pp. 1250030 ◽  
Author(s):  
R. NAECK ◽  
D. BOUNOIARE ◽  
U. S. FREITAS ◽  
H. RABARIMANANTSOA ◽  
A. PORTMANN ◽  
...  
Keyword(s):  
Time Series ◽  
Respiratory Failure ◽  
Shannon Entropy ◽  
Noninvasive Ventilation ◽  
Spontaneous Breathing ◽  
Sleep Stages ◽  
Common Procedure ◽  
Pressure Time ◽  
Ventilatory Assistance

Noninvasive ventilation is a common procedure for managing patients having chronic respiratory failure. The success of this ventilatory assistance is often linked with patient's tolerance that is known to be related to the quality of the synchronization between patient's spontaneous breathing cycles and ventilatory cycles delivered by the ventilator. Thirty-four sleep sessions (more than 5000 ventilatory cycles each) were automatically investigated using a specific algorithm processing airflow and pressure time series. Four groups of patients were defined according to the interplay between asynchrony events and leaks. Different mechanisms that depend on sleep stages were thus evidenced. A Shannon entropy was also proposed as a new sleep fragmentation quantification methodology.

Value-at-Risk and Expected Shortfall approaches for option premiums and the probability of default estimation based on ARMA models

2021 ◽  
Vol 57 (3) ◽  
pp. 126
Author(s):  
Nikolai Berzon
Keyword(s):  
Time Series ◽  
Option Pricing ◽  
Discrete Time ◽  
Value At Risk ◽  
Probability Of Default ◽  
Pricing Models ◽  
Arma Process ◽  
Underlying Asset ◽  
Black Scholes

The need to address the issue of risk management has given rise to a number of models for estimation the probability of default, as well as a special tool that allows to sell credit risk – a credit default swap (CDS). From the moment it appeared in 1994 until the crisis of 2008, that the CDS market was actively growing, and then sharply contracted. Currently, there is practically no CDS market in emerging economies (including Russia). This article is to improve the existing CDS valuation models by using discrete-time models that allow for more accurate assessment and forecasting of the selected asset dynamics, as well as new option pricing models that take into account the degree of risk acceptance by the option seller. This article is devoted to parametric discrete-time option pricing models that provide more accurate results than the traditional Black-Scholes continuous-time model. Improvement in the quality of assessment is achieved due to three factors: a more detailed consideration of the properties of the time series of the underlying asset (in particular, autocorrelation and heavy tails), the choice of the optimal number of parameters and the use of Value-at-Risk approach. As a result of the study, expressions were obtained for the premiums of European put and call options for a given level of risk under the assumption that the return on the underlying asset follows a stationary ARMA process with normal or Student's errors, as well as an expression for the credit spread under similar assumptions. The simplicity of the ARMA process underlying the model is a compromise between the complexity of model calibration and the quality of describing the dynamics of assets in the stock market. This approach allows to take into account both discreteness in asset pricing and take into account the current structure and the presence of interconnections for the time series of the asset under consideration (as opposed to the Black–Scholes model), which potentially allows better portfolio management in the stock market.

Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate

2021 ◽  
Vol 2 (3) ◽  
pp. 120-131
Author(s):  
Shaymaa Riyadh Thanoon
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Monthly Data ◽  
Number Of Patients ◽  
Auto Regressive

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

scholarly journals TESTING THE EFFECTIVENESS OF INTEGRATED ELDERLY CARE MODEL ON QUALITY OF CARE AND HEALTH OUTCOMES AMONG HOSPITALIZED ELDERLIES IN WEST BANK

2020 ◽  
Vol 20 (1) ◽  
pp. 82-89
Author(s):  
Atef H. Khatib ◽  
Ayman M. Hamdan- Mansour ◽  
Hamza F. Ratrout ◽  
Atallah Alenezi ◽  
Tala R. Chahien
Keyword(s):  
Time Series ◽  
Quality Of Care ◽  
Integrated Care ◽  
Older Patients ◽  
West Bank ◽  
Elderly Care ◽  
Multiple Time ◽  
Ageing Population ◽  
Care Model

The rapid increase in the ageing population and health conditions are imposing a higher challenge to the health care system that requires multidisciplinary teamwork utilizing coordinated care approach. This study examined the effects of integrated care model on quality of care received by older hospitalized patients in West Bank. A quantitative interrupted time series design (pretest and posttest multiple time series, quasi-experiment design) was used. The study examined the effects of integrated care model on admitted older patients (n=32) in the West Bank measuring ten dimensions of quality of care and four health indicators. There was a significant improvement in the dimensions of quality of care: dimensions: nurses’ communications with patients, physicians’ communications with patients, staff response to patients’ needs, pain management, explanations on medications, amount of information given on discharge plan, patients' area cleanliness, patients' area quietness, rating of the hospital, and willingness to recommend the hospital. Incidence of falls and incidence of pressure ulcer improved after implementing the model, while readmission rate and average length of stay did not improve. This study contributed to the limited body of knowledge related to the effect of integrated care model on hospitalized older patients’ quality of care in Palestine/ West Bank. Integrated care has the potential to improve care outcomes among hospitalized older patients.

scholarly journals Comparative analysis of the forecast for variation of the parameters of the hybrid linguistic model

2019 ◽  
Vol 3 (122) ◽  
pp. 32-41
Author(s):  
Ihor Vsevolodovych Baklan ◽  
Tetiana Viktorivna Shulkevych
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Comparative Analysis ◽  
Empirical Evidence ◽  
Optimal Parameters ◽  
Statistical Accuracy ◽  
Linguistic Model ◽  
Linguistic Approach ◽  
Linguistic Models

Using a hybrid linguistic approach to model numerical images in the form of time series using probabilistic grammars based on hidden time series and implement information technology to build sets of linguistic models and their hybrids that describe the dynamics of selected time series of processes of different nature.In the article the results of computational experiments are considered, the quality of forecasting of time series of diverse nature at various parameters was proved. The goal of the current research is to provide empirical evidence of the suitability of using a hybrid linguistic approach for predicting time series.Experimental way to find the optimal parameters of the algorithm. The algorithm was applied to a variety of time series (social, medical, financial and economic), calculated the statistical accuracy of the forecast. Experiments have shown that the algorithm consistently performs the forecast of values in a range of 3-4 steps forward and forecasts the trend change by 3-5 steps.

scholarly journals Information technology for time series forecasting by the method of the forecast scheme synthesis

2021 ◽  
Vol 3 (2) ◽  
pp. 81-86
Author(s):  
O. Yu. Mulesa ◽  
◽  
F. E. Geche ◽  
A. Ye. Batyuk ◽  
O. O. Melnyk ◽  
...  
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Linear Combination ◽  
Optimization Problem ◽  
Quality Criterion ◽  
Quality Criteria ◽  
Time Series Forecasting ◽  
Percentage Error ◽  
Forecast Models

The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes. For a given time series, the weight coefficients with which the models are included in the resulting forecast scheme are calculated by finding the solution to the optimization problem. The method of constructing the objective function for the optimization problem in the form of a linear combination of forecasting results by basic forecasting models is shown. It is proposed to find the solution to the optimization problem using a genetic algorithm. The result of the method is the forecast scheme, which is a linear combination of basic forecast models. To assess the quality of the forecast, it is suggested to use forecasting errors or forecast volatility calculated as the standard deviation. Forecast quality criteria are selected depending on the context of the task. The use of forecast volatility as a quality criterion, with repeated use of technology, will reduce the deviation of forecast values from real data. The structural scheme of information technology is developed. Structurally, information technology consists of two blocks: data processing and interpretation of the obtained values. The result of the application of the developed information technology is the production rules for determining the predicted value of the studied quantity. Experimental verification of the obtained results was performed. The problem of forecasting the number of religious organizations in Ukraine based on statistical data from 1997 to 2000 has been solved. The autoregression method and the linear regression model were chosen as the basic forecast models. Based on the results of using the developed information technology, the weights of the basic models were calculated. It is demonstrated that the obtained forecast scheme allowed to improve the average absolute percentage error and forecast volatility in comparison with the selected models. Keywords: information technology; time series; forecasting; evolutionary technologies; forecast volatility; synthesis of the forecast scheme.

scholarly journals Using the WHO Safe Childbirth Checklist to Improve Essential Care Delivery as Part of the District -Wide Maternal and Newborn Health Quality Improvement Initiative, a Time Series Study

2020 ◽  
Author(s):  
Befikadu Bitewulign ◽  
Dereje Abdissa ◽  
Zewdie Mulissa ◽  
Abiyou Kiflie ◽  
Mehiret Abate ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Improvement ◽  
Newborn Health ◽  
System Level ◽  
Maternal And Newborn Health ◽  
Safe Childbirth Checklist ◽  
Safe Childbirth ◽  
Over Time ◽  
Maternal And Newborn

Abstract Background: Care bundles are a set of three to five evidence-informed practices which, when performed collectively and reliably, may improve health system performance and patient care. To date, many studies conducted to improve the quality of essential birth care practices (EBPs) have focused primarily on provider- level and have fallen short of the predicted impact on care quality, indicating that a systems approach is needed to improve the delivery of reliable quality care. This study evaluates the effectiveness of integrating the use of the World Health Organization Safe Childbirth Checklist (WHO-SCC) into a district-wide system improvement collaborative program designed to improve and sustain the delivery of EBPs as measured by "clinical bundle" adherence over-time.Methods: The WHO-SCC was introduced in the context of a district-wide Maternal and Newborn Health (MNH) collaborative quality of care improvement program in four agrarian Ethiopia regions. Three "clinical bundles" were created from the WHO-SCC: On Admission, Before Pushing, and Soon After Birth bundles. The outcome of each bundle was measured using all- or- none adherence. Adherence was assessed monthly by reviewing charts of live births.A time-series analysis was employed to assess the effectiveness of system-level interventions on clinical bundle adherence. STATA version 13.1 was used to analyze the trend of each bundle adherence overtime. Autocorrelation was checked to assess if the assumption of independence in observations collected overtime was valid. Prais-Winsten was used to minimize the effect of autocorrelation.Findings: Quality improvement interventions targeting the three clinical bundles resulted in improved adherence over time across the four regions. In Tigray region, adherence to “On Admission” bundle was increased monthly on average by B =1.39 (95% CI; 0.47 - 2.32; P<0.005).Similarly, adherence to the “Before Pushing” bundle in Southern Nations, Nationalities and People’s (SNNP) region increased monthly on average by B = 2.3 (95% CI; 0.89 - 3.74; P<0.005). Conclusion: Use of the WHO-SCC paired with a system-wide quality improvement approach improved and sustained quality of EBPs delivery. Further studies should be conducted to evaluate the impact on patient-level outcomes.

scholarly journals Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization

2021 ◽  
Vol 3 (3) ◽  
pp. 234-248
Author(s):  
N. Bhalaji
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Deep Learning ◽  
Extreme Points ◽  
Learning Approach ◽  
Load Estimation ◽  
Learning Method ◽  
Network Computing ◽  
Noise Interference

In recent days, we face workload and time series issue in cloud computing. This leads to wastage of network, computing and resources. To overcome this issue we have used integrated deep learning approach in our proposed work. Accurate prediction of workload and resource allocation with time series enhances the performance of the network. Initially the standard deviation is reduced by applying logarithmic operation and then powerful filters are adopted to remove the extreme points and noise interference. Further the time series is predicted by integrated deep learning method. This method accurately predicts the workload and sequence of resource along with time series. Then the obtained data is standardized by a Min-Max scalar and the quality of the network is preserved by incorporating network model. Finally our proposed method is compared with other currently used methods and the results are obtained.

Effect of a health system payment and quality improvement programme for tonsillectomy in Ontario, Canada: an interrupted time series analysis

2021 ◽  
pp. bmjqs-2021-013110
Author(s):  
Sanjay Mahant ◽  
Jun Guan ◽  
Jessie Zhang ◽  
Sima Gandhi ◽  
Evan Jon Propst ◽  
...  
Keyword(s):  
Time Series ◽  
Quality Improvement ◽  
Quality Of Care ◽  
Health System ◽  
Interrupted Time Series ◽  
Improve Quality ◽  
Interrupted Time Series Analysis ◽  
Quality Improvement Programme ◽  
Improvement Programme

BackgroundTonsillectomy is among the most common and cumulatively expensive surgical procedures in children, with known variations in quality of care. However, evidence on health system interventions to improve quality of care is limited. The Quality-Based Procedures (QBP) programme in Ontario, Canada, introduced fixed episode hospital payment per tonsillectomy and disseminated a perioperative care pathway. We determined the association of this payment and quality improvement programme with tonsillectomy quality of care.MethodsInterrupted time series analysis of children undergoing elective tonsillectomy at community and children’s hospitals in Ontario in the QBP period (1 April 2014 to 31 December 2018) and the pre-QBP period (1 January 2009 to 31 January 2014) using health administrative data. We compared the age-standardised and sex-standardised rates for all-cause tonsillectomy-related revisits within 30 days, opioid prescription fills within 30 days and index tonsillectomy inpatient admission.Results111 411 children underwent tonsillectomy: 51 967 in the QBP period and 59 444 in the pre-QBP period (annual median number of hospitals, 86 (range 77–93)). Following QBP programme implementation, revisit rates decreased for all-cause tonsillectomy-related revisits (0.48 to −0.18 revisits per 1000 tonsillectomies per month; difference −0.66 revisits per 1000 tonsillectomies per month (95% CI −0.97 to −0.34); p<0.0001). Codeine prescription fill rate continued to decrease but at a slower rate (−4.81 to −0.11 prescriptions per 1000 tonsillectomies per month; difference 4.69 (95% CI 3.60 to 5.79) prescriptions per 1000 tonsillectomies per month; p<0.0001). The index tonsillectomy inpatient admission rate decreased (1.12 to 0.23 admissions per 1000 tonsillectomies per month; difference −0.89 (95% CI −1.33 to −0.44) admissions per 1000 tonsillectomies per month; p<0.0001).ConclusionsThe payment and quality improvement programme was associated with several improvements in quality of care. These findings may inform jurisdictions planning health system interventions to improve quality of care for tonsillectomy and other paediatric procedures.

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