Forecasting of demand using ARIMA model

Fattah, Jamal; Ezzine, Latifa; Aman, Zineb; Moussami, Haj El; Lachhab, Abdeslam

doi:10.1177/1847979018808673

Cited by 286 publications

(162 citation statements)

References 36 publications

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“…The ARIMA model includes autoregressive (AR) model, moving average (MA) model, and seasonal autoregressive integrated moving average (SARIMA) model [2]. The Augmented Dickey-Fuller (ADF) [3] unit-root test helps in estimating whether the time series is stationary.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Application of the ARIMA model on the COVID-2019 epidemic dataset

et al. 2020

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a b s t r a c tCoronavirus disease 2019 (COVID-2019) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic. These mathematical models based on various factors and analyses are subject to potential bias. Here, we propose a simple econometric model that could be useful to predict the spread of COVID-2019. We performed Auto Regressive Integrated Moving Average (ARIMA) model prediction on the Johns Hopkins epidemiological data to predict the epidemiological trend of the prevalence and incidence of COVID-2019. For further comparison or for future perspective, case definition and data collection have to be maintained in real time.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Application of the ARIMA model on the COVID-2019 epidemic dataset

et al. 2020

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“…The ARIMA models are important techniques in time series analysis that could be used in auto correlated data analysis. These models include autoregressive (AR) model, moving average (MA) model, and seasonal autoregressive integrated moving average (SARIMA) model (7) .…”

Section: Arima Modelsmentioning

confidence: 99%

Predicting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models in 2020

Dehesh

Mardani-Fard

Dehesh

2020

Preprint

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Background:The epidemic of a novel coronavirus illness becomes as a global threat. The aim of this study is first to find the best prediction models for daily confirmed cases in countries with high number of confirmed cases in the world and second to predict confirmed cases with these models in order to have more readiness in healthcare systems. Methods:This study was conducted based on daily confirmed cases of COVID-19 that were collected from the official website of Johns Hopkins University from January 22 th , 2020 to March 1 th , 2020. Auto Regressive Integrated Moving Average (ARIMA) model was used to predict the trend of confirmed cases. Stata version 12 were used.Results: Parameters used for ARIMA were (2,1,0) for Mainland China, ARIMA (2,2,2) for Italy , ARIMA(1,0,0) for South Korea, ARIMA (2,3,0) for Iran, and ARIMA(3,1,0) for Thailand.Mainland China and Thailand had almost a stable trend. The trend of South Korea was decreasing and will become stable in near future. Iran and Italy had unstable trends. Conclusions:Mainland China and Thailand were successful in haltering COVID-19 epidemic.Investigating their protocol in this control like quarantine should be in the first line of other countries' program

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“…It is a combination of three statistical models. An ARIMA model is denoted as an ARIMA model (p, d, q), where p is the number of autoregressive terms, d is the degree of differencing involve, and q is the number of moving-average terms [11] [12] [13]. This study follows the Box and Jenkins methodology [14], which composed of four main steps as shown in Figure 1.…”

Section: Arima Modelmentioning

confidence: 99%

Forecasting Measles Immunization Coverage Using ARIMA Model

Alegado¹,

Tumibay²

2019

JCC

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This study aimed to find a model to forecast monthly measles immunization coverage using Autoregressive Integrated Moving Average (ARIMA). The monthly registered data for measles immunization coverage from January 2014 to December 2018 were used for the development of the model. The best model with the smallest Normalized Bayesian Information Criterion (BIC) of 8.673 is ARIMA (0, 1, 0). ARIMA (0, 1, 0) was used to forecast the monthly measles immunization coverage for the next 36 months from January 2018 to December 2020. The results obtained prove that this model can be used for forecasting future immunization coverage and will help decision-makers to establish strategies, priorities, and proper use of immunization resources.

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Forecasting of demand using ARIMA model

Cited by 286 publications

References 36 publications

Application of the ARIMA model on the COVID-2019 epidemic dataset

Application of the ARIMA model on the COVID-2019 epidemic dataset

Predicting of COVID-19 Confirmed Cases in Different Countries with ARIMA Models in 2020

Forecasting Measles Immunization Coverage Using ARIMA Model

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