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.

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

Pemodelan Prakiraan Kas Pemerintah

2017 ◽  
Vol 2 (3) ◽  
pp. 1-11
Author(s):  
NFN Iskandar
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Government Policies ◽  
Forecasting Model ◽  
Cash Management ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Greek Crisis ◽  
Artificial Neural Network Ann

ABSTRACT Government cash management refers to the strategies for managing government money to fulfil governments’ obligations effectively. Failure to manage cash effectively risks undermining the implementation of government policies. The Greek crisis in 2010 is an example of a government failing to manage resources effectively. Despite the importance of effective government cash management, the literature on effective cash forecasting, as one of effective government cash management’s pillars, in the public sector is scarce. This paper addresses this shortcoming by developing a government cash forecasting model with an accuracy that meets acceptable levels of materiality for the cash manager. Using Indonesian government expenditures data in a case study, we utilise Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to build cash forecasting models. The results provide evidence that the ANN method is superior then the ARIMA model to build a government cash forecasting model. ABSTRAK Pengelolaan Kas Pemerintah mengacu pada serangkaian strategi yang dilakukan oleh pemerintah dalam mengelola uang pemerintah secara efektif dalam rangka memenuhi kewajiban pemerintah. Kegagalan dalam mengelola uang pemerintah secara efektif beresiko mengganggu pelaksanaan kebijakan pemerintah. Krisis yang dialami Yunani di tahun 2010 merupakan salah satu contoh dampak yang dapat ditimbulkan dari tidak berhasilnya suatu pemerintahan mengelola sumber daya keuangan yang mereka milik secara efektif. Terlepas dari pentingnya mengelola kas pemerintah secara efektif, literatur tentang bagaimana menyusun prakiraan kas yang efektif – sebagai salah satu pilar Pengelolaan Kas Pemerintah – bagi sektor publik masih langka. Penelitian ini bertujuan untuk mengisi kesenjangan dalam literatur dengan memperkenalkan salah satu cara menyusun model prakiraan kas pemerintah dengan tingkat akurasi yang memenuhi harapan Pengelola Kas pemerintah. Dengan menggunakan data historis harian pengeluaran pemerintah Indonesia sebagai sebuah studi kasus, penelitian ini menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Jaringan Syaraf Tiruan (JST) untuk menyusun model prakiraan kas. Penelitian ini menunjukkan bahwa penggunaan metode Jaringan Syaraf Tiruan (JST) dapat menjadi alternatif dalam menyusun model prakiraan kas pemerintah dengan tingkat akurasi model prakiraan kas yang lebih tinggi dibandingkan menggunakan ARIMA model.

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%.

scholarly journals Nonlinear ARIMA Models with Feedback SVR in Financial Market Forecasting

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shiwei Su
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Linear And Nonlinear ◽  
True Values

In recent years, as global financial markets have become increasingly connected, the degree of correlation between financial assets has become closer, and technological advances have made the transmission of information faster and faster, and information networks have integrated capital markets into one, making it easier for single financial market risk problems to form systemic risk through a high degree of market linkage effects. Based on the characteristics of financial markets containing both linear and nonlinear components, this paper chooses to use Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the ARIMA model and the SVR model, taking into account their respective linear and nonlinear characteristics. The paper chooses to use the (Autoregressive Integrated Moving Average (ARIMA) model and feedback Support Vector Regression (SVR) models to effectively integrate the strengths of the ARIMA and SVR models in terms of linearity and nonlinearity to perform forecasting analysis of financial markets. One of the important functions of forecasting is to transform future uncertainty into measurable risk, so that we can base our plans and actions on it. In this paper, the combined ARIMA-SVR model is compared with the single ARIMA model and SVR model in terms of the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), where MAE and RMSE measure the absolute error between the predicted and true values, and MAPE measures the relative error between the predicted and true values. and the relative error between the true value. The results show that the combined ARIMA-SVR model has a better forecasting effect and higher forecasting accuracy than the single ARIMA model and SVR model, and the SVR model has higher forecasting accuracy than the ARIMA model in forecasting financial markets.

scholarly journals Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Lulu Wang ◽  
Chen Liang ◽  
Wei Wu ◽  
Shengwen Wu ◽  
Jinghua Yang ◽  
...  
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
Health Delivery ◽  
Morbidity Data ◽  
Parameter Test ◽  
Model Recognition ◽  
Epidemiological Characteristics

Objective. This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. Methods. Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. Results. A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)12 model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)12 model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. Conclusion. It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)12 model.

Daily Wind Speed Forecasting Through Hybrid AR-ANN and AR-KF Models

2015 ◽  
Vol 72 (5) ◽  
Author(s):  
Osamah Basheer Shukur ◽  
Muhammad Hisyam Lee
Keyword(s):  
Wind Speed ◽  
Moving Average ◽  
Arima Model ◽  
Ar Model ◽  
Ann Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Wind Speed Forecasting ◽  
Artificial Neural Network Ann

The nonlinearity and the chaotic fluctuations in the wind speed pattern are the reasons of inaccurate wind speed forecasting results using a linear autoregressive integrated moving average (ARIMA) model. The inaccurate forecasting of ARIMA model is a problem that reflects the uncertainty of modelling process. This study aims to improve the accuracy of wind speed forecasting by suggesting more appropriate approaches. An artificial neural network (ANN) and Kalman filter (KF) will be used to handle nonlinearity and uncertainty problems. Once ARIMA model was used only for determining the inputs structures of KF and ANN approaches, using an autoregressive (AR) Instead of ARIMA may be resulted in more simplicity and more accurate forecasting. ANN and KF based on the AR model are called hybrid AR-ANN model and hybrid AR-KF model, respectively. In this study, hybrid AR-ANN and hybrid AR-KF models are proposed to improve the wind speed forecasting. The performance of ARIMA, hybrid AR-ANN, and hybrid AR-KF models will be compared to determine which had the most accurate forecasts. A case study will be carried out that used daily wind speed data from Iraq and Malaysia. Hybrid AR-ANN and AR-KF models performed better than ARIMA model while the hybrid AR-KF model was the most adequate and provided the most accurate forecasts. In conclusion, the hybrid AR-KF model will result in better wind speed forecasting accuracy than other approaches, while the performances of both hybrid models will be provided acceptable forecasts compared to ARIMA model that will provide ineffectual wind speed forecasts.

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 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.

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