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Journal articles on the topic 'BOX-JENKINS'

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Published: 4 June 2021
Last updated: 4 February 2022

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1

Kremer, Erhard. "Box-Jenkins credibility." Blätter der DGVFM 18, no. 4 (October 1988): 277–89. http://dx.doi.org/10.1007/bf02808821.

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2

Pepper, M. P. G. "Multivariate Box-Jenkins analysis." Energy Economics 7, no. 3 (July 1985): 168–78. http://dx.doi.org/10.1016/0140-9883(85)90006-4.

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3

Pintelon, R., J. Schoukens, and Y. Rolain. "Box-Jenkins Continuous-Time Modeling." IFAC Proceedings Volumes 33, no. 15 (June 2000): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)39749-5.

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4

Lu, Y., and S. M. AbouRizk. "Automated Box–Jenkins forecasting modelling." Automation in Construction 18, no. 5 (August 2009): 547–58. http://dx.doi.org/10.1016/j.autcon.2008.11.007.

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5

Pintelon, R., J. Schoukens, and Y. Rolain. "Box–Jenkins continuous-time modeling." Automatica 36, no. 7 (July 2000): 983–91. http://dx.doi.org/10.1016/s0005-1098(00)00002-9.

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6

Khalfi, Jaouad, Najib Boumaaz, Abdallah Soulmani, and El Mehdi Laadissi. "Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data." World Electric Vehicle Journal 12, no. 3 (July 28, 2021): 102. http://dx.doi.org/10.3390/wevj12030102.

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The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.
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7

Helfenstein, Ulrich. "Box-Jenkins modelling in medical research." Statistical Methods in Medical Research 5, no. 1 (March 1996): 3–22. http://dx.doi.org/10.1177/096228029600500102.

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8

Pintelon, R., P. Guillaume, and J. Schoukens. "MULTIVARIABLE FREQUENCY DOMAIN BOX-JENKINS IDENTIFICATION." IFAC Proceedings Volumes 39, no. 1 (2006): 208–13. http://dx.doi.org/10.3182/20060329-3-au-2901.00027.

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9

Piga, Dario, Valentina Breschi, and Alberto Bemporad. "Estimation of jump Box–Jenkins models." Automatica 120 (October 2020): 109126. http://dx.doi.org/10.1016/j.automatica.2020.109126.

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10

Pintelon, R., J. Schoukens, and P. Guillaume. "Box–Jenkins identification revisited—Part III." Automatica 43, no. 5 (May 2007): 868–75. http://dx.doi.org/10.1016/j.automatica.2006.11.007.

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11

Zhu, Yucai, and Håkan Hjalmarsson. "The Box–Jenkins Steiglitz–McBride algorithm." Automatica 65 (March 2016): 170–82. http://dx.doi.org/10.1016/j.automatica.2015.12.001.

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12

Al-Saati, Nabeel H., Isam I. Omran, Alaa Ali Salman, Zainab Al-Saati, and Khalid S. Hashim. "Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study." Water Practice and Technology 16, no. 2 (February 15, 2021): 681–91. http://dx.doi.org/10.2166/wpt.2021.012.

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Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the traditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE = 48.7) and the coefficient of determination = 0.801), while the (NAR) model gave (RMSE = 93.4) and = 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.
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13

Namboonruang, Weerapol, and N. Amdee. "Forecasting Technique for the Production Plan of the Local Earthenware Industries of Thailand." Applied Mechanics and Materials 577 (July 2014): 1279–82. http://dx.doi.org/10.4028/www.scientific.net/amm.577.1279.

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The purpose of this work is to compare the forecasting of time series models between two different models. One is the classical model and another is the Box-Jenkins model. The data are calculated using the circulation of Angbuaand Ahongwhich are the local earthenware products from Ratchaburi province, Thailand. Results show that the mean absolute percentage error (MAPE) of Angbua and Ahong are 17.80, 36.12 and 16.38,17.21 respectively. Also,prediction using the Box-Jenkins Model by ARIMA form of both products are (1, 0, 0) and (1, 1, 1). From this work the Box-Jenkins Model shows more appropriate method than the classical model considered by the less error.
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14

Ghoul, Yamna. "Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm." Engineering Computations 36, no. 6 (July 8, 2019): 2111–30. http://dx.doi.org/10.1108/ec-12-2018-0550.

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Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.
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15

Jere, Stanley, and Edwin Moyo. "Modelling Epidemiological Data Using Box-Jenkins Procedure." Open Journal of Statistics 06, no. 02 (2016): 295–302. http://dx.doi.org/10.4236/ojs.2016.62025.

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16

Tsoku, Johannes Tshepiso, Nonofo Phukuntsi, and Lebotsa Daniel Metsileng. "Gold sales forecasting: The Box-Jenkins methodology." Risk Governance and Control: Financial Markets and Institutions 7, no. 1 (2017): 54–60. http://dx.doi.org/10.22495/rgcv7i1art7.

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The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE). From the analysis, a seasonal ARIMA(4,1,4)×(0,1,1)12 was found to be the “best fit model” with an MAPE value of 11% indicating that the model is fit to be used to predict or forecast future gold sales for South Africa. In addition, the forecast values show that there will be a decrease in the overall gold sales for the first six months of 2014. It is hoped that the study will help the public and private sectors to understand the gold sales or output scenario and later plan the gold mining activities in South Africa. Furthermore, it is hoped that this research paper has demonstrated the significance of Box-Jenkins technique for this area of research and that they will be applied in the future.
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17

Wegman, Edward J. "Anzother look at box-jenkins forecasting procedures." Communications in Statistics - Simulation and Computation 15, no. 2 (January 1986): 523–30. http://dx.doi.org/10.1080/03610918608812522.

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18

MAKRIDAKIS, SPYROS, and MICHÈLE HIBON. "ARMA Models and the Box-Jenkins Methodology." Journal of Forecasting 16, no. 3 (May 1997): 147–63. http://dx.doi.org/10.1002/(sici)1099-131x(199705)16:3<147::aid-for652>3.0.co;2-x.

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19

Pintelon, R., and J. Schoukens. "Box–Jenkins identification revisited—Part I: Theory." Automatica 42, no. 1 (January 2006): 63–75. http://dx.doi.org/10.1016/j.automatica.2005.09.004.

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20

Pintelon, R., Y. Rolain, and J. Schoukens. "Box-Jenkins identification revisited—Part II: Applications." Automatica 42, no. 1 (January 2006): 77–84. http://dx.doi.org/10.1016/j.automatica.2005.09.005.

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21

李鹏, 李鹏. "Research of Kalman Filtering Algorithm based on Box-Jenkins Prediction." OME Information 28, no. 2 (2011): 29–32. http://dx.doi.org/10.3788/omei20112802.0029.

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22

Kuru, Merve, and Gulben Calis. "Application of time series models for heating degree day forecasting." Organization, Technology and Management in Construction: an International Journal 12, no. 1 (April 27, 2020): 2137–46. http://dx.doi.org/10.2478/otmcj-2020-0009.

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AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD.
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23

García-Jurado, I., W. González-Manteiga, J. M. Prada-Sánchez, M. Febrero-Bande, R. Cao, I. Garcia-Jurado, W. Gonzalez-Manteiga, and J. M. Prada-Sanchez. "Predicting Using Box-Jenkins, Nonparametric, and Bootstrap Techniques." Technometrics 37, no. 3 (August 1995): 303. http://dx.doi.org/10.2307/1269914.

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24

Etuk, Ette Harrison, Bartholomew Uchendu, and Ephraim Okon Udo. "Box-Jenkins Modelling of Nigerian Stock Prices Data." Greener Journal of Science, Engineering and Technological Research 2, no. 2 (September 20, 2012): 032–38. http://dx.doi.org/10.15580/gjsetr.2012.2.1211.

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25

AI.-DHOWALIA, KHALED. "Modeling Municipal Water Demand Using Box-Jenkins Technique." Journal of King Abdulaziz University-Engineering Sciences 8, no. 1 (1996): 61–71. http://dx.doi.org/10.4197/eng.8-1.5.

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26

SCHOUKENS, J., R. PINTELON, and Y. ROLAIN. "Box–Jenkins alike identification using nonparametric noise models☆." Automatica 40, no. 12 (December 2004): 2083–89. http://dx.doi.org/10.1016/s0005-1098(04)00204-3.

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27

García-Jurado, I., W. González-Manteiga, J. M. Prada-Sánchez, M. Febrero-Bande, and R. Cao. "Predicting Using Box—Jenkins, Nonparametric, and Bootstrap Techniques." Technometrics 37, no. 3 (August 1995): 303–10. http://dx.doi.org/10.1080/00401706.1995.10484336.

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28

Helfenstein, Ulrich. "Box-jenkins modelling of some viral infectious diseases." Statistics in Medicine 5, no. 1 (January 1986): 37–47. http://dx.doi.org/10.1002/sim.4780050107.

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29

Vu, K. M., P. Tessier, and G. A. Dumont. "Box–Jenkins model LQG controller: design and performance." IEE Proceedings - Control Theory and Applications 148, no. 5 (September 1, 2001): 419–29. http://dx.doi.org/10.1049/ip-cta:20010678.

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30

Hamzah, Diyana Izyan Amir, Maria Elena Nor, Sabariah Saharan, Noor Fariza Mohd Hamdan, and Nurul Asmaa Izzati Nohamad. "Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 454. http://dx.doi.org/10.14419/ijet.v7i4.30.22366.

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Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry.
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31

Schoukens, J., R. Pintelon, and Y. Rolain. "Box–Jenkins alike identification using nonparametric noise models." Automatica 40, no. 12 (December 2004): 2083–89. http://dx.doi.org/10.1016/j.automatica.2004.06.016.

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32

Nguyen-Thanh, Nhan, and Kun-Huang Huarng. "A Competitive Model to Forecast a Stock Market Index." Journal of Business Accounting and Finance Perspectives 2, no. 2 (April 14, 2020): 1. http://dx.doi.org/10.35995/jbafp2020014.

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This study proposes a competitive model using the Box–Jenkins approach to implement a Box–Jenkins ARIMA-GARCH model in order to improve financial forecasting. Differing from previous studies, we consider optimizing the lagged terms, which assist in capturing the relationships more properly. The competitive model is then used to forecast the stock market index in Taiwan. This study conducts out-of-sample forecasting and compares the root mean square errors (RMSEs) against previous studies. The results show that the competitive model outperformed in terms of both RMSEs and consistency.
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33

Salame, Camil Wadih, Joaquim Carlos Barbosa Queiroz, Everaldo Barreiros de Souza, Valcir João da Cunha Farias, Edson José Paulino da Rocha, and Helyelson Paredes Moura. "UM ESTUDO COMPARATIVO DOS MODELOS BOX-JENKINS E REDES NEURAIS ARTIFICIAIS NA PREVISÃO DE VAZÕES E PRECIPITAÇÕES PLUVIOMÉTRICAS DA BACIA ARAGUAIA, TOCANTINS, BRASIL." Revista Brasileira de Ciências Ambientais (Online), no. 52 (November 2, 2019): 28–43. http://dx.doi.org/10.5327//z2176-947820190444.

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Estudar a variabilidade dos parâmetros hidroclimáticos locais em baciashidrográficas é importante para melhorar o gerenciamento dos recursos hídricos.Para tal, foram utilizados o modelo estatístico baseado na metodologia Box-Jenkins, adotado por muitas empresas na análise de séries temporais, inclusivetodo o setor elétrico brasileiro, e a tecnologia de redes neurais, que se apresentacomo poderosa ferramenta para previsões. Na comparação entre as duas técnicas,foram utilizadas observações de médias mensais de duas estações meteorológicasda Bacia Araguaia-Tocantins, Brasil, uma de vazões mensais (m3/s) e outra deprecipitações pluviométricas mensais (mm), da Agência Nacional de Águas (ANA),com registros contínuos nos períodos de 1969 a 2017 e 1974 a 2017. As previsõesforam testadas para 12 e 24 meses. Uma comparação entre os dois métodos,usando o teste de hipótese a partir de intervalos de confiança de 95%, mostrouque não houve diferenças estatisticamente significativas nas previsões individuaistanto de precipitações pluviométricas como de vazões. Entretanto, o uso do rootmean square error (RMSE) mostrou que o método de Box-Jenkins apresentamelhores resultados. A maior dificuldade nesse método é na construção domodelo, sobretudo em séries com alta variabilidade. O método de redes neurais,em geral, consome mais tempo computacional em relação ao Box-Jenkins.
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34

Siluyele, Ian, and Stanley Jere. "Using Box-Jenkins Models to Forecast Mobile Cellular Subscription." Open Journal of Statistics 06, no. 02 (2016): 303–9. http://dx.doi.org/10.4236/ojs.2016.62026.

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35

J.T Olajide, J. T. Olajide. "Forecasting the Inflation Rate in Nigeria: Box Jenkins Approach." IOSR Journal of Mathematics 3, no. 5 (2012): 15–19. http://dx.doi.org/10.9790/5728-0351519.

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36

Maniraguha, Jean de Dieu, and Emelyne Umunoza Gasana. "Box-Jenkins Analysis of Mean Monthly Temperature in Rwanda." Afrika Statistika 13, no. 1 (January 1, 2013): 1581–92. http://dx.doi.org/10.16929/as/1581.122.

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37

Victor, Stéphane, Rachid Malti, Pierre Melchior, and Alain Oustaloup. "Instrumental Variable Identification of Hybrid Fractional Box-Jenkins Models." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4314–19. http://dx.doi.org/10.3182/20110828-6-it-1002.01107.

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38

Chowdhury, Mashfiqul Huq, Somaresh Mondal, and Jobaidul Islam. "MODELING AND FORECASTING HUMIDITY IN BANGLADESH: BOX-JENKINS APPROACH." International Journal of Research -GRANTHAALAYAH 6, no. 4 (April 30, 2018): 50–60. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1475.

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Humidity (atmospheric moisture) is an important atmospheric component and has significant influence on plant growth and development. The rate of growth and the form that a plant attains is controlled by humidity. The present study is an attempt to analyze the seasonal humidity’s of Bangladesh by employing appropriate statistical techniques. The main objective of this study is to examine humidity over time in Bangladesh and find a suitable model for forecasting. This study utilizes humidity data from Bangladesh Meteorological Department (BMD), recorded at 6 divisional meteorological stations for the period of 1976 to 2015. This study found that annual average humidity of Bangladesh is 78.88%. Initially data set is checked for whether it is stationary or not through Augmented Dickey Fuller test. Data was found non-stationary but it was transformed to stationary after taking first difference. Then seasonal ARIMA model was built using Box and Jenkins approach. After examining of all diagnostic procedures, ARIMA (2,0,1)(2,1,1)12 model has been identified as an appropriate model for forecasting 60 months (2016-2020) seasonal humidity.
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39

Nicholls, D. F. "The Box-Jenkins approach to random coefficient autoregressive modelling." Journal of Applied Probability 23, A (1986): 231–40. http://dx.doi.org/10.2307/3214355.

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Recent time series research has been directed towards the relaxation of the assumption that time series models have constant coefficients. One class of models to emerge as a result of this has been that of random coefficient autoregressive models. This paper demonstrates how the Box-Jenkins three-step approach of model specification, estimation and diagnostic checking may be applied to this class of models.
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40

Eastwood, David B., Morgan D. Gray, and John R. Brooker. "Forecasting Item Movement with Scan Data: Box-Jenkins Results." Northeastern Journal of Agricultural and Resource Economics 20, no. 1 (April 1991): 42–51. http://dx.doi.org/10.1017/s0899367x00002841.

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Preliminary forecasts using the Box-Jenkins methodology for supermarket scan data for ground beef and roast item movement are described. The functional form and the accuracy of the forecasts vary by product. Results suggest that further analyses incorporating price and advertising may increase the accuracy of the forecasts.
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41

Nicholls, D. F. "The Box-Jenkins approach to random coefficient autoregressive modelling." Journal of Applied Probability 23, A (1986): 231–40. http://dx.doi.org/10.1017/s0021900200117103.

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Recent time series research has been directed towards the relaxation of the assumption that time series models have constant coefficients. One class of models to emerge as a result of this has been that of random coefficient autoregressive models. This paper demonstrates how the Box-Jenkins three-step approach of model specification, estimation and diagnostic checking may be applied to this class of models.
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42

Löhnberg, P., and M. C. Vlot. "Recursive Triple Least Squares Identification for Box-jenkins Models." IFAC Proceedings Volumes 21, no. 9 (August 1988): 483–88. http://dx.doi.org/10.1016/s1474-6670(17)54774-6.

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43

KUVAT, Özlem, and Ege ADALI. "Power transformer demand forecast with Box Jenkins ARIMA model." International Journal of Energy Applications and Technologies 7, no. 3 (October 5, 2020): 95–100. http://dx.doi.org/10.31593/ijeat.771010.

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44

Aljamaan, Ibrahim A., Abdullah S. Bu bshait, and David T. Westwick. "Separable Least Squares Identification of Wiener Box-Jenkins Models." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4434–39. http://dx.doi.org/10.3182/20110828-6-it-1002.03676.

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45

Ding, Feng, and Honghong Duan. "Two‐stage parameter estimation algorithms for Box–Jenkins systems." IET Signal Processing 7, no. 8 (October 2013): 646–54. http://dx.doi.org/10.1049/iet-spr.2012.0183.

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46

Raja, Muhammad Asif Zahoor, and Naveed Ishtiaq Chaudhary. "Adaptive strategies for parameter estimation of Box–Jenkins systems." IET Signal Processing 8, no. 9 (December 2014): 968–80. http://dx.doi.org/10.1049/iet-spr.2013.0438.

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47

Wang, Dongqing, Guowei Yang, and Ruifeng Ding. "Gradient-based iterative parameter estimation for Box–Jenkins systems." Computers & Mathematics with Applications 60, no. 5 (September 2010): 1200–1208. http://dx.doi.org/10.1016/j.camwa.2010.06.001.

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48

Pflaumer, Peter. "Forecasting US population totals with the Box-Jenkins approach." International Journal of Forecasting 8, no. 3 (November 1992): 329–38. http://dx.doi.org/10.1016/0169-2070(92)90051-a.

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49

Rosel, Jesús, and Eduardo Elósegui. "Daily and Weekly Smoking Habits: A Box-Jenkins Analysis." Psychological Reports 75, no. 3_suppl (December 1994): 1639–48. http://dx.doi.org/10.2466/pr0.1994.75.3f.1639.

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The objective of this research was to ascertain if there are different temporal patterns of smoking. The method of data collection was to use voluntary subject smokers who recorded their daily cigarette consumption for 84 days. Subjects had smoked more than 5 cigarettes per day throughout the previous year; 29 subjects kept accurate autorecords. The daily smoking data of each subject were analyzed via the time-series procedure ARIMA(p,d,q)(P,D,Q) S of Box and Jenkins. 15 subjects (52%) showed simple autoregressive smoking models for which smoking on any given day was a function of the number of cigarettes smoked on the previous day or days, but 13 subjects (45%) showed autoregressive models of weekly seasonality, i.e., the number of cigarettes smoked on any given day is a function of the number smoked on the same day of the previous week, and only 1 subject's data (3%) had unpredictable smoking patterns.
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50

Smartson. P. NYONI and Thabani NYONI. "OPEN DEFECATION IN MALAWI: A BOX-JENKINS ARIMA APPROACH." Middle European Scientific Bulletin 5 (October 13, 2020): 156–65. http://dx.doi.org/10.47494/mesb.2020.5.82.

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Abstract:
Using annual time series data on the number of people who practice open defecation in Malawi from 2000 – 2017, the study predicts the annual number of people who will still be practicing open defecation over the period 2018 – 2021. The study applies the Box-Jenkins ARIMA methodology. The diagnostic ADF tests show that the M series under consideration is an I (1) variable. Based on the AIC, the study presents the ARIMA (3, 1, 0) model as the optimal model. The diagnostic tests further show that the presented model is stable and its residuals are stationary in levels. The results of the study indicate that the number of people practicing open defecation in Malawi is likely to decline, over the period 2018 – 2022, from approximately 5.1% to almost 2.8% of the total population. Indeed, by 2030, open defecation can be eliminated in Malawi: hence, the country is in the right track with regards to its vision 2030 (on water, sanitation and hygiene). The study suggested a 3-fold policy recommendation to be put into consideration, especially by the government of Malawi.
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