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.

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 Forecasting monthly farm tractor demand for India using MSARIMA and ARMAX models

2019 ◽  
Author(s):  
Alok Yadav ◽  
Sajal Ghosh
Keyword(s):  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Farm Tractor ◽  
The World ◽  
Moving Average Model

Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management. Keywords

Short term Forecast of the COVID-19 Epidemic in top 15 Affected Countries in the World using ARIMA Model with Machine Learning Approach (Preprint)

2020 ◽  
Author(s):  
Pavan Kumar ◽  
Ranjit Sah ◽  
Alfonso J. Rodriguez-Morales ◽  
Himangshu Kalita Jr ◽  
Akshaya Srikanth Bhagavathula ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moving Average ◽  
Arima Model ◽  
The United States ◽  
Learning Approach ◽  
Average Model ◽  
Machine Learning Approach ◽  
The World ◽  
Moving Average Model ◽  
Auto Regressive

BACKGROUND The COVID-19 pendemic reached more than 200 countries, which was recognized during December-19 from CHINA and affected more than 28 lakh people on date April 26, 2020 (data source:Johns Hopkins Corona Virus Resource Center). OBJECTIVE We here predicted some trajectories of COVID-19 in the coming days (until July 2, 2020) using the most advanced Auto-Regressive Integrated Moving Average Model (ARIMA). METHODS Here we have used the Auto-Regressive Integrated Moving Average Model (ARIMA). Mathematical approaches are widely used to infer critical epidemiological transitions and parameters of COVID-19. Methods such as epidemic curve fitting, surveillance data during the early transmission R0, and other epidemic models are frequently applied to generate forecasts of COVID-19 pandemic across the world. RESULTS Our analysis predicted very frightening outcomes, which defines to worsen the conditions in Iran, entire Europe, especially Italy, Spain, and France. While South Korea, after the initial blast, has come to stability, the same goes for the COVID-19 origin country China with more positive recovery cases and confirm to remain stable. The United States of America (USA) is come as a surprise and going to become the epicenter for new cases during the mid-April 2020. CONCLUSIONS Based on our predictions, public health officials should tailor aggressive interventions to grasp the power exponential growth, and rapid infection control measures at hospital levels are urgently needed to curtail the COVID-19 pandemic. This study analyzed at global level and extracted data upon Machine Learning approach using Artificial intelligence techniques for top 10% or 20 countries.

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 A statistical study of COVID-19 pandemic in Egypt

2021 ◽  
Vol 54 (1) ◽  
pp. 233-244
Author(s):  
Taha Radwan
Keyword(s):  
Linear Relationship ◽  
Statistical Study ◽  
Moving Average ◽  
Arima Model ◽  
Decision Makers ◽  
Accurate Data ◽  
Autoregressive Integrated Moving Average ◽  
The World ◽  
The Future

Abstract The spread of the COVID-19 started in Wuhan on December 31, 2019, and a powerful outbreak of the disease occurred there. According to the latest data, more than 165 million cases of COVID-19 infection have been detected in the world (last update May 19, 2021). In this paper, we propose a statistical study of COVID-19 pandemic in Egypt. This study will help us to understand and study the evolution of this pandemic. Moreover, documenting of accurate data and taken policies in Egypt can help other countries to deal with this epidemic, and it will also be useful in the event that other similar viruses emerge in the future. We will apply a widely used model in order to predict the number of COVID-19 cases in the coming period, which is the autoregressive integrated moving average (ARIMA) model. This model depicts the present behaviour of variables through linear relationship with their past values. The expected results will enable us to provide appropriate advice to decision-makers in Egypt on how to deal with this epidemic.

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