scholarly journals Comparison of Time Series Prediction of Healthcare Emergency Department Indicators with ARIMA and Prophet

2019 ◽  
Author(s):  
Diego Duarte ◽  
Julio Faerman
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Prediction

Neural Network Techniques for Time Series Prediction: A Review

2019 ◽  
Vol 3 (3) ◽  
Author(s):  
Muhammad Faheem Mushtaq ◽  
Urooj Akram ◽  
Muhammad Aamir ◽  
Haseeb Ali ◽  
Muhammad Zulqarnain
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Series Data ◽  
Prediction Problem ◽  
Neural Network Models ◽  
Physical Time

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.

Making investment decisions in stock markets using a forecasting-Markowitz based decision-making approaches

2019 ◽  
Vol 15 (2) ◽  
pp. 647-659 ◽  
Author(s):  
Zahra Moeini Najafabadi ◽  
Mehdi Bijari ◽  
Mehdi Khashei
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Stock Markets ◽  
Time Series Prediction ◽  
Investment Decisions ◽  
Rate Of Return ◽  
Expected Returns ◽  
Prediction Methods ◽  
Content Type ◽  
Markowitz Model

Purpose This study aims to make investment decisions in stock markets using forecasting-Markowitz based decision-making approaches. Design/methodology/approach The authors’ approach offers the use of time series prediction methods including autoregressive, autoregressive moving average and artificial neural network, rather than calculating the expected rate of return based on distribution. Findings The results show that using time series prediction methods has a significant effect on improving investment decisions and the performance of the investments. Originality/value In this study, in contrast to previous studies, the alteration in the Markowitz model started with the investment expected rate of return. For this purpose, instead of considering the distribution of returns and determining the expected returns, time series prediction methods were used to calculate the future return of each asset. Then, the results of different time series methods replaced the expected returns in the Markowitz model. Finally, the overall performance of the method, as well as the performance of each of the prediction methods used, was examined in relation to nine stock market indices.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

scholarly journals COVID-19 Time Series Prediction

2021 ◽  
Vol 181 ◽  
pp. 973-980
Author(s):  
Leonardo Sestrem de Oliveira ◽  
Sarah Beatriz Gruetzmacher ◽  
João Paulo Teixeira
Keyword(s):  
Time Series ◽  
Time Series Prediction

scholarly journals 1120. Impact of Training Residents to Improve HIV Screening in a Teaching Hospital in Mexico City

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S590-S590
Author(s):  
Lorena Guerrero-Torres ◽  
Isaac Núñez-Saavedra ◽  
Yanink Caro-Vega ◽  
Brenda Crabtree-Ramírez
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Hiv Testing ◽  
Teaching Hospital ◽  
Educational Intervention ◽  
Temporal Trends ◽  
Hiv Screening ◽  
Hiv Diagnosis ◽  
Post Intervention ◽  
Hiv Test

Abstract Background Among 230,000 people living with HIV in Mexico, 24% are unaware of their diagnosis, and half of newly diagnosed individuals are diagnosed with advanced disease. Early diagnosis is the goal to mitigate HIV epidemic. Missed opportunities may reflect a lack of clinicians’ consideration of HIV screening as part of routine medical care. We assessed whether an educational intervention on residents was effective to 1) improve the knowledge on HIV screening; 2) increase the rate of HIV tests requested in the hospitalization floor (HF) and the emergency department (ED); and 3) increase HIV diagnosis in HF and ED. Methods Internal Medicine and Surgery residents at a teaching hospital were invited to participate. The intervention occurred in August 2018 and consisted in 2 sessions on HIV screening with an expert. A questionnaire was applied before (BQ) and after (AQ) the intervention, which included HIV screening indications and clinical cases. The Institutional Review Board approved this study. Written informed consent was obtained from all participants. BQ and AQ scores were compared with a paired t-test. To evaluate the effect on HIV test rate in the HF and ED, an interrupted time series analysis was performed. Daily rates of tests were obtained from September 2016 to August 2019 and plotted along time. Restricted cubic splines (RCS) were used to model temporal trends. HIV diagnosis in HF and ED pre- and post-intervention were compared with a Fisher’s exact test. A p< 0.05 was considered significant. Results Among 104 residents, 57 participated and completed both questionnaires. BQ score was 79/100 (SD±12) and AQ was 85/100 (SD±8), p< .004. Time series of HIV testing had apparent temporal trends (Fig 1). HIV test rate in the HF increased (7.3 vs 11.1 per 100 episodes) and decreased in the ED (2.6 vs 2.3 per 100 episodes). HIV diagnosis increased in the HF, from 0/1079 (0%) pre-intervention to 5/894 (0.6%) post-intervention (p< .018) (Table 1). Fig 1. HIV test rates. Gray area represents post-intervention period. Table 1. Description of episodes, HIV tests and rates pre- and post-intervention in the Emergency Department and Hospitalization Floor. Conclusion A feasible educational intervention improved residents’ knowledge on HIV screening, achieved maintenance of a constant rate of HIV testing in the HF and increased the number of HIV diagnosis in the HF. However, these results were not observed in the ED, where administrative barriers and work overload could hinder HIV screening. Disclosures All Authors: No reported disclosures

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