Estimating Perturbation and Meta-Stability in the Daily Attendance Rates of Six Small High Schools

2017 ◽  
Vol 16 (03) ◽  
pp. 1750021 ◽  
Author(s):  
Matthijs Koopmans
Keyword(s):  
Time Series ◽  
High Schools ◽  
Moving Average ◽  
Attendance Rates ◽  
Attendance Rate ◽  
Small High Schools ◽  
Educational Literature ◽  
Daily Attendance ◽  
Vantage Points ◽  
The Impact

This paper discusses the daily attendance rates in six small high schools over a ten-year period and evaluates how stable those rates are. “Stability” is approached from two vantage points: pulse models are fitted to estimate the impact of sudden perturbations and their reverberation through the series, and Autoregressive Fractionally Integrated Moving Average (ARFIMA) techniques are used to detect dependencies over the long range of the series. The analyses are meant to (1) exemplify the utility of time series approaches in educational research, which lacks a time series tradition, (2) discuss some time series features that seem to be particular to daily attendance rate trajectories such as the distinct downward pull coming from extreme observations, and (3) present an analytical approach to handle the important yet distinct patterns of variability that can be found in these data. The analysis also illustrates why the assumption of stability that underlies the habitual reporting of weekly, monthly and yearly averages in the educational literature is questionable, as it reveals dynamical processes (perturbation, meta-stability) that remain hidden in such summaries.

scholarly journals Exploring the Effects of Creating Small High Schools on Daily Attendance: A Statistical Case Study

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Matthijs Koopmans
Keyword(s):  
Time Series ◽  
High Schools ◽  
Time Series Analysis ◽  
Policy Research ◽  
Initial Increase ◽  
Post Intervention ◽  
Small High Schools ◽  
Series Analysis ◽  
School Year ◽  
Daily Attendance

Does creating small high schools have a beneficial impact on daily attendance? This question was addressed using time series analysis to examine the case of one urban transfer high school that serves students who previously dropped out of school. This analytical approach is uniquely suitable to examine the dynamical processes characterizing stability and transformation in the system. This school reduced its size from enrolling approximately 900 students up to and through the 2009-2010 school year to about 250 students afterward. We looked at whether attendance was higher after the intervention and whether it was more stable. It turns out that the attendance trajectories over a seven-year period show high volatility prior to the reduction in school size but are more stable afterward. The initial increase in daily attendance at the onset of the intervention is not maintained, but increases are observed later. The study illustrates the relevance of time series analysis for educational policy research as well as the use of complexity theory to fully appreciate the nature of the post intervention changes.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Intervention Time Series Modeling of Infant Mortality: Impact of Free Maternal Health Care

2020 ◽  
pp. 38-47
Author(s):  
J. Kisabuli ◽  
J. Ong'ala ◽  
E. Odero
Keyword(s):  
Health Care ◽  
Time Series ◽  
Maternal Health ◽  
Infant Mortality ◽  
Moving Average ◽  
Maternal Health Care ◽  
The Sustainable Development ◽  
Infant Deaths ◽  
Moving Average Model ◽  
The Impact

Infant mortality is an important marker of the overall society health. The 3rd goal of the Sustainable Development Goals aims at reducing infant deaths that occur due to preventable causes by 2030. Due to increased infant mortality the Kenyan government introduced Free Maternal Health Care as an intervention towards reducing infant mortality through elimination of the cost burden of accessing medical care by the mother and the infant. The study examines the impact of Free Maternal Health Care on infant mortality using Intervention time series analysis particularly the intervention Box Jenkins ARIMA (Autoregressive Integrated Moving Average) model. There was significant support that Free Maternal Health Care had a significant impact on infant mortality which was estimated to be a decrease of 10.15% in infant deaths per month.

scholarly journals The time series regression analysis in evaluating the economic impact of COVID-19 cases in Indonesia

2021 ◽  
Vol 16 (3) ◽  
pp. 197-210
Author(s):  
Utriweni Mukhaiyar ◽  
Devina Widyanti ◽  
Sandy Vantika
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Series Regression ◽  
Vector Autoregressive ◽  
Daily Data ◽  
The Impact

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

scholarly journals COVID-19 pandemic control using restrictions and vaccination

2021 ◽  
Vol 19 (2) ◽  
pp. 1355-1372
Author(s):  
Vinicius Piccirillo ◽  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Real Data ◽  
The United States ◽  
Prevention Policy ◽  
Autoregressive Integrated Moving Average ◽  
Infected People ◽  
Marquardt Algorithm ◽  
The Impact ◽  
Methods Numerical

<abstract><p>This work deals with the impact of the vaccination in combination with a restriction parameter that represents non-pharmaceutical interventions measures applied to the compartmental SEIR model in order to control the COVID-19 epidemic. This restriction parameter is used as a control parameter, and the univariate autoregressive integrated moving average (ARIMA) is used to forecast the time series of vaccination of all individuals of a specific country. Having in hand the time series of the population fully vaccinated (real data + forecast), the Levenberg–Marquardt algorithm is used to fit an analytic function that models this evolution over time. Here, it is used two time series of real data that refer to a slow vaccination obtained from India and Brazil, and two faster vaccination as observed in Israel and the United States of America. Together with vaccination, two different control approaches are presented in this paper, which enable reduces the infected people successfully: namely, the feedback and nonfeedback control methods. Numerical results predict that vaccination can reduce the peaks of infections and the duration of the pandemic, however, a better result is achieved when the vaccination is combined with any restrictions or prevention policy.</p></abstract>

scholarly journals Trend analysis of rainfall using Mann-Kendall, Sen’s slope, moving average and least square techniques for Ahmednagar district of Maharashtra

2021 ◽  
Vol 17 (1) ◽  
pp. 19-25
Author(s):  
Virendra N. Barai ◽  
Rohini M. Kalunge
Keyword(s):  
Time Series ◽  
Trend Analysis ◽  
Moving Average ◽  
Rainfall Variability ◽  
Least Square ◽  
Annual Rainfall ◽  
Series Data ◽  
Sen’S Slope ◽  
Increasing Trend ◽  
The Impact

The long-term behaviour of rainfall is necessary to study over space with different time series viz., annual, monthly and weekly as it is one of the most significant climatic variables. Rainfall trend is an important tool which assesses the impact of climate change and provides direction to cope up with its adverse effects on the agriculture. Several studies have been performed to establish the pattern of rainfall over various time periods for different areas that can be used for better agricultural planning, water supply management, etc. Consequently, the present report, entitled “Trend analysis of rainfall in Ahmednagar district of Maharashtra,” was carried out. 13 tahsils of the district of Ahmednagar were selected to carry out trend analysis. The daily rainfall data of 33 years (1980- 2012) of all stations has been processed out study the rainfall variability. The Mann Kendall (MK) Test, Sen’s slope method, moving average method and least square method were used for analysis. The statistical analysis of whole reference time series data highlighted that July and August month contributes highest amount of rainfall at all tahsils. Regarding trend in annual rainfall, these four methods showed increasing trend at most of the tahsils whereas a decreasing trend only at Shrigonda tahsil. For monthly trend analysis, Kopargaon, Newasa, Shevgaon and Shrirampur tahsils showed an increasing trend during July. During August and September month, most of the tahsils i.e. Kopargaon, Nagar, Parner and Sangamner showed increasing trends, whereas in June, only Shrigonda tahsil showed decreasing trend.

scholarly journals Assessment of the Impact of COVID-19 pandemic on population level interest in Skincare: Evidence from a google trends-based Infodemiology study

2020 ◽  
Author(s):  
Hasan Symum ◽  
Md. F. Islam ◽  
Habsa K. Hiya ◽  
Kh M. Ali Sagor
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Population Level ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Search Volume ◽  
Quasi Experimental ◽  
Relative Search Volume ◽  
The Impact

AbstractBackgroundCOVID-19 pandemic created an unprecedented disruption of daily life including the pattern of skin related treatments in healthcare settings by issuing stay-at-home orders and newly coronaphobia around the world.ObjectiveThis study aimed to evaluate whether there are any significant changes in population interest for skincare during the COVID-19 pandemic.MethodsFor the skincare, weekly RSV data were extracted for worldwide and 23 counties between August 1, 2016, and August 31, 2020. Interrupted time-series analysis was conducted as the quasi-experimental approach to evaluate the longitudinal effects of COVID-19 skincare related search queries. For each country, autoregressive integrated moving average (ARIMA) model relative search volume (RSV) time series and then testing multiple periods simultaneously to examine the magnitude of the interruption. Multivariate linear regression was used to estimate the correlation between countries’ relative changes in RSV with COVID-19 confirmed cases/ per 10000 patients and lockdown measures.ResultsOut of 23 included countries in our study, 17 showed significantly increased (p<0.01) RSVs during the lockdown period compared with the ARIMA forecasted data. The highest percentage of increments occurs in May and June 2020 in most countries. There was also a significant correlation between lockdown measures and the number of COVID-19 cases with relatives changes in population interests for skincare.ConclusionUnderstanding the trend and changes in skincare public interest during COVID-19 may assist health authorities to promote accessible educational information and preventive initiatives regarding skin problems.

Analysis of Excess Deaths During the COVID-19 Pandemic in the State of Florida

2021 ◽  
Vol 111 (4) ◽  
pp. 704-707 ◽  
Author(s):  
Moosa Tatar ◽  
Amir Habibdoust ◽  
Fernando A. Wilson
Keyword(s):  
Time Series ◽  
Moving Average ◽  
The State ◽  
Time Series Modeling ◽  
Mortality Trends ◽  
Historical Trends ◽  
Autoregressive Integrated Moving Average ◽  
The Impact ◽  
Excess Deaths

Objectives. To determine the number of excess deaths (i.e., those exceeding historical trends after accounting for COVID-19 deaths) occurring in Florida during the COVID-19 pandemic. Methods. Using seasonal autoregressive integrated moving average time-series modeling and historical mortality trends in Florida, we forecasted monthly deaths from January to September of 2020 in the absence of the pandemic. We compared estimated deaths with monthly recorded total deaths (i.e., all deaths regardless of cause) during the COVID-19 pandemic and deaths only from COVID-19 to measure excess deaths in Florida. Results. Our results suggest that Florida experienced 19 241 (15.5%) excess deaths above historical trends from March to September 2020, including 14 317 COVID-19 deaths and an additional 4924 all-cause, excluding COVID-19, deaths in that period. Conclusions. Total deaths are significantly higher than historical trends in Florida even when accounting for COVID-19–related deaths. The impact of COVID-19 on mortality is significantly greater than the official COVID-19 data suggest.

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