scholarly journals FORECASTING OF UNMET NEEDS PERCENTAGE IN EAST JAVA PROVINCE USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD

2020 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Feri Styaningsih
Keyword(s):  
Unmet Needs ◽  
Moving Average ◽  
Arima Model ◽  
Unmet Need ◽  
Small Error ◽  
Monthly Data ◽  
Specific Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Test Requirements

ARIMA uses present and past values as the dependent variable. The accuracy of the ARIMA forecasting method results is good to be used to obtain short-term forecasts. Compared to other time series methods, the advantage of ARIMA method is that it can be used in the percentage of unmet needs data in East Java Province since ARIMA method does not require any specific data motives. Unmet need is a group of women who do not want to have any more children or want to minimize their pregnancy but refuse to use contraception to prevent pregnancy. This study aims to determine the percentage of unmet needs in East Java Province in the future. This study will analyze the value of forecasting and determine the best model for ARIMA. The data used is the monthly data of unmet needs percentage of East Java Province starting from January 2014 to April 2019 (64 data plots). The results showed that the percentage of the number of unmet needs in East Java Province can be predicted using ARIMA model (12,1,0) without constant. The model is based on ARIMA (12,1,0) diagnostic test without constant meeting all the test requirements. The results of forecasting held a MAPE value of 2.369% and MAE of 0.26%. Based on MAPE and MAE, the model has a very good forecasting ability with a fairly small error value. Forecasting results indicated fluctuations in unmet needs data, where from December 2019 to February 2020 there was an increase in number of unmet needs in East Java Province. In the interim, starting in March 2020, the data needs in East Java Province tend to be constant at a higher position than the previous increase.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals A Forecasting Method Based on ARIMA Model for Best-Fitted Nutrition Water Supplement on Fruits

2019 ◽  
Vol 9 (2) ◽  
pp. 3167-3173
Keyword(s):  
Water Level ◽  
Moving Average ◽  
Arima Model ◽  
Environmental Parameters ◽  
Maximum Temperature ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Feature Vectors ◽  
Forecasting Method ◽  
Water Rate

The main focus of this research is to promote a forecasting method in the greenhouse of cultivation for the nutrition water level of strawberry fruits. In the greenhouse of cultivation, this study selects strawberry fruits as the focus on research. With adequate nutrition water supply conditions, the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA-SARIMA) were utilized to create forecasting for the nutrition water level of strawberry leaves in the fruit greenhouse of cultivation, thus forecasting strawberry's nutrition water rate through greenhouse environmental parameters. Next, the multi-scale feature vectors of greenhouse temperature and nutrition water parameters in the greenhouse have been extracted by using the data pre-processing method to eliminate the testing and training value of variables, thus improving the forecasting and generalization ability of the model. The extracted feature vectors have been used to train and optimize the SARIMA model, finally obtaining the forecasting model of nutrition water rate of strawberry fruits leaves in the greenhouse of cultivation, which has been compared in experiments with the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA - SARIMA) model. The results indicate that when training samples become a certain amount, the forecasting accuracy and regression fitting degree of ARIMA - SARIMA can be higher than that of the two traditional models. We forecasted that the strawberry greenhouse included 233 samples collected from a strawberry greenhouse in South Korea, and the 6 variables involved are greenhouse maximum temperature, greenhouse minimum temperature, greenhouse average temperature, quality of nutrient water, humanity, and CO2 , which would influence the strawberry growth in production concentration directly or indirectly with the variation of nutrition water every day.

scholarly journals Aplikasi Autoregressive Integrated Moving Average (ARIMA) untuk Meramalkan Jumlah Demam Berdarah Dengue (DBD) di Puskesmas Mulyorejo

2017 ◽  
Vol 5 (2) ◽  
pp. 177
Author(s):  
Luluk Nor Kasanah
Keyword(s):  
Public Health ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Moving Average ◽  
Arima Model ◽  
Hemorrhagic Fever ◽  
Small Error ◽  
Least Square ◽  
Public Health Center ◽  
Forecasting Method

ARIMA was one of a forecasting method of time series if independent variable be ignored, it would use the past and present value as a dependent variable. The accuracy of ARIMA forecasting method was good to produce short-term forecasting. The advantages of ARIMA method than other method was this method didn’t require the data pattern so it could be used for all kinds of data pattern, so it could be applied in cases of dengue hemorrhagic fever (DHF) in Mulyorejo Public Health Center. This study was to determine the best forecasting model as well as to predict and analyze the results of forecasting number of dengue hemorrhagic fever in Mulyorejo Public Health Center. The data was monthly number of dengue hemorrhagic fever patients in Mulyorejo Public Health Center from January 2010 to February 2016 (a total of 74 plots data). The results were the number of dengue hemorrhagic fever cases in Mulyorejo Public Health Center could be predicted with ARIMA model (1,0,0), thought based on diagnostics test the ARIMA model met all tests but the forecasting number of dengue hemorrhagic fever cases in years 2016–2017 showed a downward trend, and in 2017 was fl at, while MAPE and MAE amounted to 63.026% and 1.89%, the value of the error was large enough which indicated that less accurate forecasting. DHF data had a lot of missing data caused big value of MAPE and MAE so must be transformed by series mean method. DHF data was trend and seasonal so winters exponential smoothing with ordinary least square was better than ARIMA to get small error.

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.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Seawater intrusion vulnerability evaluation and prediction: a case study of Qeshm Island, Iran

2020 ◽  
Author(s):  
Amin Zeynolabedin ◽  
Reza Ghiassi ◽  
Moharram Dolatshahi Pirooz
Keyword(s):  
Seawater Intrusion ◽  
Moving Average ◽  
Arima Model ◽  
Chloride Ion ◽  
Absolute Error ◽  
Ion Concentration ◽  
Qeshm Island ◽  
Autoregressive Integrated Moving Average ◽  
Dangerous Condition

Abstract Seawater intrusion is one of the most serious issues to threaten coastal aquifers. Tourian aquifer, which is selected as the case study, is located in Qeshm Island, Persian Gulf. In this study, first the vulnerability of the region to seawater intrusion is assessed using chloride ion concentration value, then by using the autoregressive integrated moving average (ARIMA) model, the vulnerability of the region is predicted for 14 wells in 2018. The results show that the Tourian aquifer experiences moderate vulnerability and the area affected by seawater intrusion is wide and is in danger of expanding. It is also found that 0.95 km2 of the region is in a state of high vulnerability with Cl concentration being in a dangerous condition. The prediction model shows that ARIMA (2,1,1) is the best model with mean absolute error of 13.3 mg/L and Nash–Sutcliffe value of 0.81. For fitted and predicted data, mean square error is evaluated as 235.3 and 264.3, respectively. The prediction results show that vulnerability is increasing through the years.

A Forecasting Method for Fertilizers Consumption in Brazil

2013 ◽  
Vol 4 (2) ◽  
pp. 23-36 ◽  
Author(s):  
Eduardo Ogasawara ◽  
Daniel de Oliveira ◽  
Fabio Paschoal Junior ◽  
Rafael Castaneda ◽  
Myrna Amorim ◽  
...  
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Logistic Function ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
The World ◽  
Entire Country ◽  
Function Models ◽  
Nitrogen Phosphorus

Tracking information about fertilizers consumption in the world is very important since they are used to produce agriculture commodities. Brazil consumes a large amount of fertilizers due to its large-scale agriculture fields. Most of these fertilizers are currently imported. The analysis of consumption of major fertilizers, such as Nitrogen-Phosphorus-Potassium (NPK), Sulfur, Phosphate Rock, Potash, and Nitrogen become critical for long-term government decisions. In this paper we present a method for fertilizers consumption forecasting based on both Autoregressive Integrated Moving Average (ARIMA) and logistic function models. Our method was used to forecast fertilizers consumption in Brazil for the next 20 years considering different economic growth for the entire country.

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