SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines

Malki, Amer; Atlam, El‐Sayed; Hassanien, Aboul Ella; Ewis, Ashraf A.; Dagnew, Guesh; Gad, Ibrahim

doi:10.1016/j.aej.2022.05.051

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…The results of this study are in accordance with three previous studies which state that the SARIMA method is the right method to be used on seasonal time series data. The results of this study are also in accordance with the research of Falatouri et al (2022), Kumar Dubey et al (2021, Malki et al (2022), andMuthu et al (2021) which states that the SARIMA method is an accurate, precise, and suitable model to be applied in seasonal forecasting. This study provides results that have never been done in previous research, namely forecasting the number of ship passengers departing and arriving at Semayang Harbor.…”

Section: G Accuracy Of Forecasting Resultssupporting

confidence: 87%

“…The SARIMA model is widely applied to predict seasonal time series data, such as to predict cases of tuberculosis (Mao et al, 2018), predict cases of malaria (Permanasari et al, 2013), predict the composition of iterations of coal Hardgrove grindability index (HGI) (Dindarloo et al, 2016), predict the number of covid-19 vaccines needed (Malki et al, 2022), predict consumer price index (Muthu et al, 2021), and others. SARIMA model is an accurate, precise, and suitable model to be applied in forecasting seasonal time series data (Bas et al, 2017;Dindarloo et al, 2016;Falatouri et al, 2022;Kumar Dubey et al, 2021;Malki et al, 2022;Mao et al, 2018;Muthu et al, 2021;Shen & Chen, 2017). SARIMA provides better forecasting results than other models (ArunKumar et al, 2021;He et al, 2021;Hu et al, 2007;H.…”

Section: A Introductionmentioning

confidence: 99%

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Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City)

Multiningsih

Siswanah

Saleh

2022

JTAM

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From year to year, the number of ship passengers at Semayang Port, Balikpapan city tends to fluctuate. It also doubles in certain months and repeats every year. Sea transportation companies need to make forecasts in order to implement policies related to predict the number and capacity of ships that need to be provided as well as the preparation of port facilities. The study aims at obtaining the best model, predicting and determining the accuracy of the forecasting results for the number of passengers arriving and departing at Semayang Port, Balikpapan city using SARIMA method. The SARIMA method is a time series data forecasting method that is able to identify seasonal patterns. The results showed that the best model for predicting the number of passengers departing at Semayang Port, Balikpapan city is the SARIMA (4,1,0)(0,1,2)12 model with a MAPE of 14.05%. It means that the SARIMA model used produces good forecasting. Meanwhile, the best model to predict the number of passengers coming to Semayang Port Balikpapan city is the SARIMA (0,1,1)(2,1,0)12 model with a MAPE value of 3.27% which exposes that the SARIMA model used succeed to provide accurate forecasting. The results of this forecast can be used as a reference for the government or port managers to anticipate a surge in passengers. The government or port management can prepare an adequate amount of transportation in certain months to avoid the accumulation of passengers and to make sea transportation more efficient.

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Section: G Accuracy Of Forecasting Resultssupporting

confidence: 87%

Section: A Introductionmentioning

confidence: 99%

Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City)

Multiningsih

Siswanah

Saleh

2022

JTAM

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“…SARIMA adalah perluasan dari model ARIMA yang dapat mengakomodasi pola tren dan musiman dalam data deret waktu [2], [14]. SARIMA memiliki orde (p, d, q) (P, D, Q) s di mana p, d, dan q mewakili komponen non-musiman, P, D, dan Q mewakili komponen musiman, dan s mewakili frekuensi pola musiman.…”

Section: Seasonal Autoregressive Integrated Moving Average (Sarima)unclassified

Model Gabungan (Ansambel) Sarima Dan Jaringan Saraf Tiruan Untuk Peramalan Beban Listrik

Silfiani

2023

variance

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This study aims to investigate the efficacy of employing artificial neural networks in conjunction with a seasonal autoregressive integrated moving average (SARIMA) ensemble for forecasting electrical load. The SARIMA ensemble comprises members generated by varying autoregressive orders or moving averages. Subsequently, these SARIMA ensemble members are integrated using artificial neural networks. The datasets encompass monthly electrical load data pertaining to households, businesses, industries, and the public, spanning from January 2016 to December 2020. The findings demonstrate that across various categories, SARIMA ensemble-based artificial neural networks demonstrated superior predictive performance compared to alternative models. Future research endeavors should focus on exploring diverse methodologies for both creating and amalgamating ensemble members.

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“…Another option is using SARIMA models, an extension to Autoregressive Integrated Moving Average (ARIMA) models that explicitly support the direct modelling of the seasonal component of time series data and can incorporate exogenous variables. Contrary to ARIMA, it also uses past values and considers any seasonality pattern; therefore, it is more potent than ARIMA when forecasting complex data containing periodicity (Malki et al, 2022). Applications in meteorology and water sciences exist (Azad et al, 2022;Belotti et al, 2021;Chang et al, 2013;Chen et al, 2018;Oktaviani et al, 2021), but to our knowledge, modelling monthly river temperatures still need to be included.…”

Section: Introductionmentioning

confidence: 99%

Monthly stream temperatures along the Danube River: Statistical analysis and predictive modelling with incremental climate change scenarios

Pekárová,

Bajtek,

Pekár

et al. 2023

Journal of Hydrology and Hydromechanics

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The aim of the study is to analyse changes and predict the course of mean monthly water temperatures of the Danube River at various locations for the future. The first part of the study involves conducting a statistical analysis of the annual and monthly average air temperatures, water temperatures, and discharges along the Danube River. The study examines long-term trends, changes in the trends, and multiannual variability in the time series. The second part of the study focuses on simulating the average monthly water temperatures using Seasonal Autoregressive Integrated Moving Average (SARIMA) models and nonlinear regression models (NonL), based on two RCP based incremental mean monthly air temperature scenarios. To assess the impact of future climate on stream temperatures, the historical long-term average of the monthly water temperature (1990–2020) was compared with scenarios S1 (2041–2070) and S2 (2071–2100). The simulation results from the two stochastic models, the SARIMA and NonL, showed that in scenario S1, the Danube River’s average monthly water temperature is projected to increase by 0.81/0.82°C (Passau), 0.55/0.71°C (Bratislava), and 0.68/0.56°C (Reni). In scenario S2, the models predict higher increases: 2.83/2.50°C (Passau), 2.06/2.46°C (Bratislava), and 2.52/1.90°C (Reni). Overall, the SARIMA model proved to be more stable and effective in simulating the increase in monthly water temperatures in the Danube River.

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SARIMA model-based forecasting required number of COVID-19 vaccines globally and empirical analysis of peoples’ view towards the vaccines

Cited by 11 publications

References 39 publications

Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City)

Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City)

Model Gabungan (Ansambel) Sarima Dan Jaringan Saraf Tiruan Untuk Peramalan Beban Listrik

Monthly stream temperatures along the Danube River: Statistical analysis and predictive modelling with incremental climate change scenarios

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