scholarly journals Forecasting and predicting intussusception in children younger than 48 months in Suzhou using a seasonal autoregressive integrated moving average model

BMJ Open ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. e024712
Author(s):  
Wan-liang Guo ◽  
Jia Geng ◽  
Yang Zhan ◽  
Ya-lan Tan ◽  
Zhang-chun Hu ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Early Warning Systems ◽  
Early Summer ◽  
Warning Systems ◽  
Epidemiological Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Treatment And Prevention ◽  
Moving Average Model ◽  
Modelling Process

ObjectiveThe aims of this study were to highlight some epidemiological aspects of intussusception cases younger than 48 months and to develop a forecasting model for the occurrence of intussusception in children younger than 48 months in Suzhou.DesignA retrospective study of intussusception cases that occurred between January 2007 and December 2017.SettingRetrospective chart reviews of intussusception paediatric patients in a large Children’s hospital in South-East China were performed.ParticipantsThe hospital records of 13 887 intussusception cases in patients younger than 48 months were included in this study.InterventionsThe modelling process was conducted using the appropriate module in SPSS V.23.0.MethodsThe Box-Jenkins approach was used to fit a seasonal autoregressive integrated moving average (ARIMA) model to the monthly recorded intussusception cases in patients younger than 48 months in Suzhou from 2007 to 2016.ResultsEpidemiological analysis revealed that intussusception younger than 48 months was reported continuously throughout the year, with peaks in the late spring and early summer months. The most affected age group was younger than 36 months. The time-series analysis showed that an ARIMA (1,0,1 1,1,1)12model offered the best fit for surveillance data of intussusception younger than 48 months. This model was used to predict intussusception younger than 48 months for the year 2017, and the fitted data showed considerable agreement with the actual data.ConclusionARIMA models are useful for monitoring intussusception in patients younger than 48 months and provide an estimate of the variability to be expected in future cases in Suzhou. The models are helpful for predicting intussusception cases in Suzhou and could be useful for developing early warning systems. They may also play a key role in early detection, timely treatment and prevention of serious complications in cases of intussusception.

scholarly journals Forecasting GDP Movements in Nepal Using Autoregressive Integrated Moving Average (ARIMA) Modelling Process

2019 ◽  
Vol 4 (2) ◽  
pp. 1-20
Author(s):  
Surya Bahadur Rana
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Sample Data ◽  
Moving Average Model ◽  
Annual Time Series ◽  
Annual Time ◽  
Modelling Process

This study attempts to test the ARIMA model and forecast annual time series of GDP in Nepal from mid-July, 1960 to mid-July, 2018. The annual time series on GDP used in this study consists of total 59 observations. Out of them, three years’ data from mid-July 2016 to mid-July 2018 have been used for in-sample forecasting and evaluation. The study uses univariate Box-Jenkins ARIMA modelling process to identify the best fitted model that describes the sample data set. The study examines a number of ARIMA family models and recommends ARIMA (0,1,2) as the most appropriate model that best describes the annual GDP series of the sampled period. The ARIMA (0, 1, 2) model incorporates zero lag order for autoregression, integrated with 2 lag order for moving average model using first difference operator. The ARIMA model forecasts documented in this study are not significantly different from actual because the actual annual GDP series observed in forecast period fall within 95 per cent confidence interval of estimates. Hence, ARIMA (0,1,2) model can best capture the GDP movement in Nepal for the sample period.

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.

Autoregressive Integrated Moving Average Model to Predict Graduate Unemployment in Indonesia

2017 ◽  
Vol 12 (1) ◽  
pp. 43-50
Author(s):  
Umi Mahmudah
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model ◽  
Forecasting Analysis ◽  
Graduate Unemployment ◽  
Point Forecast

AbstractNowadays it is getting harder for higher education graduates in finding a decent job. This study aims to predict the graduate unemployment in Indonesia by using autoregressive integrated moving average (ARIMA) model. A time series data of the graduate unemployment from 2005 to 2016 is analyzed. The results suggest that ARIMA (1,2,0) is the best model for forecasting analysis, where there is a tendency of increasing number for the next ten periods. Furthermore, the average of point forecast for the next 10 periods is about 1,266,179 while its minimum value is 1,012,861 the maximum values is 1,523,156. Overall, ARIMA (1,2,0) provides an adequate forecasting model so that there is no potential for improvement.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

scholarly journals Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Dwi Asa Verano ◽  
Husnawati Husnawati ◽  
Ermatita Ermatita
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Raw Material ◽  
Digital Printing ◽  
Printing Industry ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

Dwell Time Prediction of Bus Rapid Transit Using ARIMA-SVM Hybrid Model

2014 ◽  
Vol 587-589 ◽  
pp. 1993-1997 ◽  
Author(s):  
Jian Ding ◽  
Min Yang ◽  
Yi Cao ◽  
Si Li Kong
Keyword(s):  
Dwell Time ◽  
Moving Average ◽  
Linear Part ◽  
Arima Model ◽  
Bus Rapid Transit ◽  
Autoregressive Integrated Moving Average ◽  
Nonlinear Part ◽  
Svm Model ◽  
Moving Average Model ◽  
Short Time

The short-time dwell time of BRT is hard to predict. Considering impacts of complex traffic environment, we can predict the value more effectively by using a new hybrid method, which is mixed with ARIMA (Autoregressive Integrated Moving Average Model), predicting the self-relevant linear part and SVM, predicting residual nonlinear part, than the single ARIMA model and SVM model. The dwell times of BRT line1in Chang Zhou have proved this thesis.

Modelling international tourism demand using seasonal ARIMA models

2015 ◽  
Vol 21 (1) ◽  
pp. 19-31
Author(s):  
Tea Baldigara ◽  
Maja Mamula
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Tourism Demand ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Diagnostic Checking ◽  
Tourism Sector ◽  
Proposed Model ◽  
Moving Average Model ◽  
Seasonal Arima Models

Purpose – The purpose of this study is to establish a seasonal autoregressive integrated moving average model able to capture and explain the patterns and the determinants of German tourism demand in Croatia. Design – The present study is based on the Box-Jenkins approach in building a seasonal autoregressive integrated moving average model intend to describe the behaviour of the German tourists’ flows to Croatia. Approach – The proposed model is a seasonal ARIMA(0,0,0)(1,1,3)4 model. Findings – The diagnostic checking and the performed tests showed that the estimated seasonal ARIMA(0,0,0)(1,1,3)4 model is adequate in modelling and analysing the number of German tourists ‘arrivals to Croatia. Originality of the paper – This study provides a seasonal ARIMA model helpful to analyse, understand and forecast German tourists’ flows to Croatia. Such, more detailed and systematic studies should be considered as starting points of future macroeconomic development strategies, pricing strategies and tourism sector routing strategies in Croatia, as a predominantly tourism oriented country.

scholarly journals An An Approach to Measure the Death Impact of Covid-19 in Jakarta using Autoregressive Integrated Moving Average (ARIMA)

2020 ◽  
Vol 9 (2) ◽  
pp. 108-116
Author(s):  
Ferdian Fadly ◽  
Erika Sari
Keyword(s):  
Forecast Error ◽  
Moving Average ◽  
Arima Model ◽  
Average Model ◽  
Real Situation ◽  
Autoregressive Integrated Moving Average ◽  
The Real ◽  
The People ◽  
The World ◽  
Moving Average Model

Coronavirus disease 2019 (COVID-19) is a pandemic in more than 200 countries around the world. As the fourth most populous nation in the world, Indonesia is predicted to face a big threat to this pandemic particularly Jakarta as the epicenter of the virus in Indonesia. However, the nature of COVID-19 that can easily spread and also many undetected cases that do not present symptoms make it more difficult to determine the real mortality effects of COVID-19.The deaths in Jakarta from the new coronavirus may be higher than officially reported. To overcome this issue, this paper will provide an approach to measure the death impact of COVID-19 using the Autoregressive Integrated Moving Average model (ARIMA). The model will predict the ‘what if’ normal condition of the number of funerals in Jakarta compared to the real situation in March 2020 as an approach of the actual effect of COVID-19 in Jakarta. This research revealed a discrepancy of 450-1070 funerals in March 2020 that could not be predicted by the ARIMA model. This funeral gap, a forecast error, could be an approach to the potential number of possible death impacts of COVID-19 in Jakarta that should be significantly higher than the report. The people should be more conscious and alert of COVID-19 situation.

scholarly journals Autoregressive Integrated Moving Average Model for Polar Seas Ice Prediction

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Sea Ice ◽  
Moving Average ◽  
Arima Model ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Autoregressive Integrated Moving Average ◽  
Antarctic Sea Ice ◽  
Research Systems ◽  
Moving Average Model ◽  
Set Up

Sea ice predictions are very important for the future of polar climates and play a significant role in ecosystems. Models are the simulated representations that have been set up to research systems. To advance model forecasts, researchers require improved parameterizations that are formed by the assembling and analysis of convenient observations. In this study, an Autoregressive Integrated Moving Average (ARIMA) model is proposed to predict the Arctic and Antarctic sea ice extent. The data is gathered from the National Snow and Ice Center (NSIDC) between 01. Jan.1979 and 30. Jun.2020. The fitted data between 2017 and 2020 matches the observed data very closely with the overlap is firmly within the 95% confidence band shows the success of the model.

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