scholarly journals ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD

2021 ◽  
Vol 2021 (60) ◽  
pp. 51-57
Author(s):  
P.V. Shymaniuk ◽  
◽  
V.O. Miroshnyk ◽  
Keyword(s):  
United States ◽  
Time Series ◽  
Comparative Analysis ◽  
Decomposition Methods ◽  
The United States ◽  
Data Validation ◽  
Clustering Methods ◽  
Short Term ◽  
Northwestern Region ◽  
Short Term Forecasting

A comparative analysis of clustering methods was performed to identify gaps and anomalous values in the data. Data from the northwestern region of the United States were used for evaluation. According to the analysis results, it was found that the use of the DBSCAN method leads to a much smaller number of false positives. An algorithm for two-stage data validation using clustering and time series decomposition methods is proposed. Ref.9, fig. 3, tables 3.

scholarly journals The Forecast of COVID-19 Spread Risk at The County Level

2021 ◽  
Author(s):  
Murtadha Hssayeni ◽  
Arjuna Chala ◽  
Roger Dev ◽  
Lili Xu ◽  
Jesse Shaw ◽  
...  
Keyword(s):  
United States ◽  
Short Term Memory ◽  
Absolute Error ◽  
The United States ◽  
Lessons Learned ◽  
County Level ◽  
Short Term ◽  
Mobility Data ◽  
Deep Model ◽  
Short Term Forecasting

Abstract The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people's lives and restart the economy quickly and safely. People’s social behavior as captured by their mobility data plays a role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreak in the United States. The daily data are fed to a deep model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p=0.005)) between the model prediction and the actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. Lower correlation was reported for the counties with a total cases of <1,000 during the test interval. The average mean absolute error (MAE) was 605.4, and it was decreasing with the decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread where average daily cases decrease with the decrease in retires percentage, and increase with the increase in young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also could also help with early and effective management of possible future pandemics.

Time Series Analysis of The Novel Coronavirus (COVID-19)

2020 ◽  
Author(s):  
Bin Zhao
Keyword(s):  
United States ◽  
Time Series ◽  
Arima Model ◽  
The United States ◽  
Hubei Province ◽  
Cumulative Number ◽  
The Novel ◽  
Short Term ◽  
The Real ◽  
Novel Coronavirus

Background: Since the first appearance of the novel coronavirus in Wuhan in December 2019, it has quickly swept the world and become a major security incident facing humanity today. While the novel coronavirus threatens people's lives and safety, the economies of various countries have also been severely damaged. Due to the epidemic, a large number of enterprises have faced closures, employment has become more difficult, and people's lives have been greatly affected. Therefore, to establish a time series model for Hubei Province, where the novel coronavirus first broke out, and the United States, where the epidemic is most severe, to analyze the spreading trend and short-term forecast of the new coronavirus, which will help countries better understand the development trend of the epidemic and make more adequate preparation and timely intervention and treatment to prevent the further spread of the virus. Methods: For the data collected from Hubei Province, including cumulative diagnoses, cumulative deaths, and cumulative cures, we use SPSS to establish the time series model. Since there is no problem of missing data values, we define days as the time variable, remove outliers, and set the width of the confidence interval to 95% for prediction, then use SPSS's expert modeler to find the best-fit model for each sequence. ACF, PACF graphs of the residuals, and Q-tests are used to determine whether the residuals are white noise sequences and to check whether the model is a suitable model. Holt model is used for the cumulative number of diagnoses, and ARIMA (1,2,0) model is used for cumulative cures and deaths. Similarly, we also collect data for the US, including the cumulative number of diagnoses, cumulative deaths, and cumulative cures. For the three groups mentioned above, ARIMA (2,2,6) model, ARIMA (0,2,0) model, and ARIMA (0,2,1) model is used respectively. Findings: From our modeling of the data, the time series diagrams of the real the fitted data almost overlap, so the fitting effect of the Holt model and the ARIMA model we use is very suitable. We compare the predicted values with the real values of the same period and find that the epidemic situation in Hubei Province has basically ended after May, but the epidemic situation in the United States has become more severe after May, so the Holt model and the ARIMA model are also very appropriate in predicting the epidemic situation in short-term. Interpretation: Because the Chinese government has always put the safety of people’s lives in the first place, when the epidemic broke out, it decisively closed the city of Hubei Province. One side is in trouble, all sides support, they concentrate all resources of whole country to save Hubei Province at the expense of the economy only in order to save more people. Now we can clearly see that the epidemic has been controlled in China and the whole country is developing in a good direction. The situation in the United States, on the other hand, is also influenced by the social environment.

scholarly journals The forecast of COVID-19 spread risk at the county level

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Murtadha D. Hssayeni ◽  
Arjuna Chala ◽  
Roger Dev ◽  
Lili Xu ◽  
Jesse Shaw ◽  
...  
Keyword(s):  
United States ◽  
Short Term Memory ◽  
Absolute Error ◽  
The United States ◽  
Lessons Learned ◽  
County Level ◽  
Short Term ◽  
Mobility Data ◽  
Daily Data ◽  
Short Term Forecasting

AbstractThe early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved (r=0.83 (p = 0.005)) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk.

Cultural Awareness of Students in Communication Sciences and Disorders: A Mixed-Methods Study

2020 ◽  
Vol 5 (5) ◽  
pp. 1231-1242
Author(s):  
Celeste Domsch ◽  
Lori Stiritz ◽  
Jay Huff
Keyword(s):  
United States ◽  
Study Abroad ◽  
Cultural Awareness ◽  
Qualitative Data ◽  
The United States ◽  
Self Report ◽  
Global Awareness ◽  
Short Term ◽  
Term Study ◽  
Short Term Study

Purpose This study used a mixed-methods design to assess changes in students' cultural awareness during and following a short-term study abroad. Method Thirty-six undergraduate and graduate students participated in a 2-week study abroad to England during the summers of 2016 and 2017. Quantitative data were collected using standardized self-report measures administered prior to departure and after returning to the United States and were analyzed using paired-samples t tests. Qualitative data were collected in the form of daily journal reflections during the trip and interviews after returning to the United States and analyzed using phenomenological methods. Results No statistically significant changes were evident on any standardized self-report measures once corrections for multiple t tests were applied. In addition, a ceiling effect was found on one measure. On the qualitative measures, themes from student transcripts included increased global awareness and a sense of personal growth. Conclusions Measuring cultural awareness poses many challenges. One is that social desirability bias may influence responses. A second is that current measures of cultural competence may exhibit ceiling or floor effects. Analysis of qualitative data may be more useful in examining effects of participation in a short-term study abroad, which appears to result in decreased ethnocentrism and increased global awareness in communication sciences and disorders students. Future work may wish to consider the long-term effects of participation in a study abroad for emerging professionals in the field.

Comparative analysis of the trade policy of Barack Obama and Donald Trump

2020 ◽  
pp. 59-71
Author(s):  
V. Iordanova ◽  
A. Ananev
Keyword(s):  
United States ◽  
Economic Development ◽  
Comparative Analysis ◽  
Trade Policy ◽  
World Economy ◽  
The United States ◽  
Barack Obama ◽  
Scientific Article ◽  
Donald Trump ◽  
Entire World

The authors of this scientific article conducted a comparative analysis of the trade policy of US presidents Barack Obama and Donald Trump. The article states that the tightening of trade policy by the current President is counterproductive and has a serious impact not only on the economic development of the United States, but also on the entire world economy as a whole.

The Future of Muslim Education in the United States: An Agenda for Research

2003 ◽  
Vol 20 (3-4) ◽  
pp. 46-82
Author(s):  
Fathi Malkawi
Keyword(s):  
United States ◽  
The United States ◽  
American Education ◽  
Future Trends ◽  
Muslim Community ◽  
Short Term ◽  
The Future ◽  
Muslim Education ◽  
Future Plans

This paper addresses some of the Muslim community’s concerns regarding its children’s education and reflects upon how education has shaped the position of other communities in American history. It argues that the future of Muslim education will be influenced directly by the present realities and future trends within American education in general, and, more importantly, by the well-calculated and informed short-term and long-term decisions and future plans taken by the Muslim community. The paper identifies some areas in which a wellestablished knowledge base is critical to making decisions, and calls for serious research to be undertaken to furnish this base.

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