scholarly journals A Time Series Model to Forecast Covid-19 Infection rate in Nigeria Using Box-Jenkins Method

2021 ◽  
Vol 4 (1) ◽  
pp. 83-98
Author(s):  
CA Ortese ◽  
TG Ieren ◽  
AJ Tamber
Keyword(s):  
Task Force ◽  
Moving Average ◽  
Unit Root Test ◽  
Future Trend ◽  
Suitable Model ◽  
Test Parameter ◽  
Autoregressive Integrated Moving Average ◽  
Global Pandemic ◽  
Augmented Dickey Fuller ◽  
One Step

Coronavirus declared as a global pandemic by WHO has emerged as the most aggressive disease negatively affecting more than 90% countries of the world. Nigeria, one of the most populated countries in Africa is not an exception. This study focuses on analyzing the intrinsic patterns in the COVID-19 spread in Nigeria using the Box-Jenkins procedure. Data of daily confirmed cases of COVID-19 in Nigeria was retrieved from Nigeria Centre for Disease Control (NCDC) official website from February 27, 2020 to October 31, 2020 to identify the series components, estimate parameters, develop an appropriate stochastic predictive model and use the model to forecast future trend of the deadly virus. The Autoregressive Integrated Moving Average (ARIMA) of order (0,1,1) was identified as the most suitable model based on the analysis of the autocorrelation (ACF), partial autocorrelation functions (PACF) and Akaike Information Correction (AICc) value. R software version 4.0.3 was used to analyze the trend which moothen the series by using 8-point moving average to extract the irregular component as wellas differencing the series one step further to obtain a stationary series. We performed the Augmented Dickey-Fuller Unit root test, parameter estimation and Ljung-Box test to check the proposed model’s conformity to the stationary univariate process. A 85 – day (1st Oct., 2020 – 24th Jan., 2020)forecast shows a gradual decline in the successive number of confirmed cases of infection indicating the effectiveness of the intervention strategies employed by the Task Force to contain the virus. The concerned authorities can apply the forecasted trend to make further informed decisions on the measures to be put in place to reduce diffusion of the deadly virus into the country.

scholarly journals Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure

2020 ◽  
Author(s):  
Rauf Rauf Ibrahim ◽  
Hannah Oluwakemi Oladipo
Keyword(s):  
Task Force ◽  
Moving Average ◽  
Arima Model ◽  
Time Frame ◽  
R Software ◽  
Test Parameter ◽  
Autoregressive Integrated Moving Average ◽  
Stationarity Test ◽  
Parameter Test ◽  
Insight Into

AbstractObjectiveThis study is focused on the analysis of the spread of Covid-19 in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection.MethodologyDaily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria.ResultsThe ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box–Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame.ConclusionFederal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of Covid-19 in Nigeria.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Forecasting Monthly Gold Prices using ARIMA Model: Evidence from Indian Gold Market

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1373-1376
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Gold Price ◽  
Data Series ◽  
Suitable Model ◽  
Natural Logarithm ◽  
Price Series ◽  
Gold Market ◽  
Sample Data ◽  
Augmented Dickey Fuller

In this paper an attempt has been made to give an overview of the Indian gold market so as to develop a model enabling the forecast of gold prices in India. One troy ounce is equal to 31.103 grams. The monthly sample data of gold price (in INR per troy ounce) is taken from December 1997 to December 2017.The entire data has been divided into two segments for estimation and validation sample and to find out the efficiency and accuracy of forecasting models. Since the gold price data series have shown much deviation after March 2006 the first segment of the data is taken from the time period of December 1997 to March 2006 and second segment from April 2006 to December 2017.Due to a larger value and a huge time span of the sample data, the natural logarithm of gold price has been taken to conduct the study and build an effective model to forecast future gold prices. The unit root tests of Augmented Dickey Fuller‖ and Philips Perron have been used to test the gold price series as stationary or non-stationary. It is observed that series are stationary at first difference in both the methods. At first difference the ACFs and PACFs were pattern less and statistically not significant. Box-Jenkins’s Autoregressive Integrated Moving Average of Box-Jenkins methodology has been used for developing a forecasting model of gold price in India. Different models of ARIMA have been used to obtain best suitable model for forecasting using Eviews software 10 for both time periods i.e., December 1997 to March 2006 & April 2006 to December 2017

scholarly journals Forecasting Import Demand of Table Grapes: Empirical Evidence from Thailand

2020 ◽  
Vol 10 (2) ◽  
pp. 578-586
Author(s):  
Chalermpon Jatuporn ◽  
Patana Sukprasert ◽  
Siros Tongchure ◽  
Vasu Suvanvihok ◽  
Supat Thongkaew
Keyword(s):  
Time Series ◽  
Empirical Evidence ◽  
Unit Root ◽  
Moving Average ◽  
Unit Root Test ◽  
Import Demand ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Table Grapes ◽  
Monthly Time Series

The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.

A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Kehinde Adekunle Bashiru
Keyword(s):  
Process Model ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Probability Generating Function ◽  
Information Criteria ◽  
Data Series ◽  
Suitable Model ◽  
Birth Process ◽  
Autoregressive Integrated Moving Average

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series

scholarly journals Epidemiological Prognosis of Pertussis Incidence in Bulgaria

Folia Medica ◽  
2020 ◽  
Vol 62 (3) ◽  
pp. 509-514
Author(s):  
Ralitsa Raycheva ◽  
Yordanka Stoilova ◽  
Ani Kevorkyan ◽  
Vanya Rangelova
Keyword(s):  
Time Series ◽  
Health Management ◽  
Whooping Cough ◽  
Moving Average ◽  
Real Life ◽  
Acceptable Error ◽  
Autoregressive Integrated Moving Average ◽  
One Step ◽  
Original Time ◽  
Near Future

Introduction: Epidemiological forecasting facilitates scientifically sound solutions to upcoming theoretical and practical issues, in the development of public health management, in particular of infectious diseases. Aim: To critically analyze the most recent scientific advances in the biosocial nature and methodology of epidemiological forecasting to present a real-life example of pertussis, a disease with shifting epidemiology. Materials and methods: For the prediction of pertussis morbidity the autoregressive integrated moving average (ARIMA) the model was established by utilizing the method of time series analysis to construct a model of overall morbidity using Time series modeller in SPSS v.25. To model pertussis morbidity we obtained official data from the Ministry of Health and the National Center for Infectious and Parasitic Diseases, since the beginning of disease registration from 1903 until 2018. We also analyzed the shifting epidemiology of pertussis. Results: The proper identification procedures we applied indicated ARIMA (3,0,0) model to best fit our original time series of the annual whooping cough morbidity for the 1921-2018 period. The model predicts better morbidity in a one-step forecast. The incidence rate is expected to be stable at about 1.35 per 100,000 in the next three years, which is close to the 2016 level and lower than those in 2017-2018. Conclusion: The ARIMA (3,0,0) model in our study is an adequate tool for presenting the pertussis morbidity trend and is suitable to forecast near-future disease dynamics, with acceptable error tolerance.

scholarly journals Evaluating Data-Driven Forecasting Methods for Predicting SARS-CoV2 Cases: Evidence From 173 Countries

2020 ◽  
Author(s):  
Ghufran Ahmad ◽  
Furqan Ahmed ◽  
Suhail Rizwan ◽  
Javed Muhammad ◽  
Hira Fatima ◽  
...  
Keyword(s):  
Moving Average ◽  
Data Availability ◽  
Data Driven ◽  
Percentage Error ◽  
Pictorial Representation ◽  
Limited Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Methods ◽  
Global Pandemic ◽  
Future Burden

AbstractThe WHO announced the epidemic of SARS-CoV2 as a public health emergency of international concern on 30th January 2020. To date, it has spread to more than 200 countries, and has been declared as a global pandemic. For appropriate preparedness, containment, and mitigation response, the stakeholders and policymakers require prior guidance on the propagation of SARS-CoV2. This study aims to provide such guidance by forecasting the cumulative COVID-19 cases up to 4 weeks ahead for 173 countries, using four data-driven methodologies; autoregressive integrated moving average (ARIMA), exponential smoothing model (ETS), random walk forecasts (RWF) with and without drift. We also evaluate the accuracy of these forecasts using the Mean Absolute Percentage Error (MAPE). The results show that the ARIMA and ETS methods outperform the other two forecasting methods. Additionally, using these forecasts, we generated heat maps to provide a pictorial representation of the countries at risk of having an increase in cases in the coming 4 weeks for June. Due to limited data availability during the ongoing pandemic, less data-hungry forecasting models like ARIMA and ETS can help in anticipating the future burden of SARS-CoV2 on healthcare systems.

scholarly journals Autoregressive Integrated Moving Average (ARIMA-Box Jenkins) Pada Peramalan Komoditas Cabai Merah di Indonesia

2021 ◽  
Vol 5 (2) ◽  
pp. 485
Author(s):  
Ridha Maya Faza Lubis ◽  
Zakarias Situmorang ◽  
Rika Rosnelly
Keyword(s):  
Market Share ◽  
Moving Average ◽  
Central Bureau ◽  
Autoregressive Integrated Moving Average ◽  
Price Fluctuations ◽  
Weight Value ◽  
Augmented Dickey Fuller ◽  
Stationarity Test ◽  
Adf Test ◽  
Selection Of

Chili is one of the main staples in making a dish and chili is one of the values in a commodity that has superior value, the price of chili often experiences price fluctuations or what is known as the price which is always changing. data taken from BPS (Central Bureau of Statistics) data nationally from January 2001 to December 2015 data, this study also aims to be able to predict national chili prices which will later be used in research, namely discussing the Autoregressive Integrated Moving Average (ARIMA) method. In this study, the identification of the model was carried out using two tests, namely the stationarity test and the correlation test. The stationarity test is the Augmented Dickey-Fuller (ADF) test, the Philips-Perron (PP) test and the Kwiatkowski-Philips-Schmidt-Shin (KPPS) test using Minitab 9.The chili commodity is a very important commodity in the Indonesian economy, because In terms of consumption, chilies have a very significant market share, which can be seen from data from the Central Statistics Agency (BPS) with an inflation weight value of 0.35%. From the research, it was found that for the selection of the best method, namely ARIMA (3,1,0) because it has the smallest MSE value and the forecasting results for the next 12 periods in January 2016 ranged from Rp. 11,868.2 to Rp. 28,315.5 and so on until December 2016.

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

scholarly journals Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model.

2021 ◽  
pp. 129-148
Author(s):  
Raad Mozib Lalon ◽  
Nusrat Jahan
Keyword(s):  
Autocorrelation Function ◽  
Economic Performance ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Unit Root Test ◽  
Information Criterion ◽  
Gdp Growth ◽  
Autoregressive Integrated Moving Average ◽  
Q Model

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model. We have found the appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of Bangladesh for next couple of years adopting Box-Jenkins approach to construct the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World Bank Data stream from 1961 to 2019. JEL classification numbers: B22, B23, C53. Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.

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