How to cite this paper
Ighravwe, D & Anyaeche, C. (2019). A comparison of ARIMA and ANN techniques in predicting port productivity and berth effectiveness.International Journal of Data and Network Science, 3(1), 13-22.
Refrences
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Anyaeche, C.O., & Ighravwe, D.E. (2013). Predicting performance measures using linear regression and neural network: A comparison. African Journal of Engineering Research, 1(3), 84-89.
Bhardwaj, S.P. (2015). Market intelligence and price forecast. Retrieved April, 12, 2015, from www.iasri.res.in/cbp/data.
Blackard, J.A., & Dean, D.J. (1999). Comparative accuracies of artificial neural networks and discrimi-nant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24, 131-151.
Bretas, A.S., & Phadke, A.G. (2003). Artificial neural networks in power system restoration. IEEE Transactions on Power Delivery, 18(4), 1181-1186.
Cao, Q., & Schniederjans, M.J. (2006). Agent-mediated architecture for reputation-based electronic tourism systems: A neural network approach. Information & Management, 43, 598-606.
Contreras, J., Espinola, R., Nogales, F.J., & Conejo, A.J. (2003). ARIMA Models to Predict next-Day Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014-1020.
Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction. John Wiley and Son Ltd.
Felipe, I.J.d-S, Mol, A.L.R., & e-Almeida, V.d-S. (2012). Application of ARIMA models in soybean series of prices in the north of Parana. Custos e @gronegócio on line, 8, 78-91.
Hakimpoor, H., & Arshad, K.A.B. (2011). Artificial neural networks’ applications in management. World Applied Sciences Journal, 14 (7), 1008-1019.
Indraratna, B., Rujikiatkamjorn, C., Geng, X., Ameratunga, J., & Boyle, P. (2011). Performance and prediction of vacuum combined surcharge consolidation at port of Brisbane, Coastal and Marine Geotechnics: Foundations for Trade, Sydney: Australia, 45-60.
Kamruzzaman, J., Sarker, R.A., & Begg, R. (2006). Artificial neural networks: Applications in finance and manufacturing, In Kamruzzaman J., Begg R. and Sarker R.A. (Eds.). Artificial neural networks: Applications in finance and manufacturing, Idea Group publishing, London.
Kamruzzaman, J., & Sarker, R.A. (2003). Comparing ANN based models with ARIMA for prediction of forex rates. ASOR Bulletin, 22(2), 1-10.
Kumar, Y., Das, B., & Sharma, J. (2011). Application of ANN in service restoration in distribution sys-tems with noisy input. IPCSIT, 7, 247-252.
Lee, M.H., Abd. Rahman, N.H., Suhartono, Latif, M.T., Nor, M.E., & Kamisan N.A.B. (2012). Sea-sonal ARIMA for forecasting air pollution index: A Case Study. American Journal of Applied Sci-ences, 9(4), 570-578.
Li, X., & Moller, M. (2009). Applying GLM model and ARIMA model to the analysis of monthly tem-perature of Stockholm. Department of Economics and Society, Dalarna University, 1-24.
Merh, N., Saxena, V.P., & Pardasani K.R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Business Intelligence Journal, 3(2), 23 - 43.
Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models economic Analysis. Research And Publications Department, Central Bank of Ireland.
Moradi, M., & Zulkernine, M., (2004). A neural network based system for intrusion detection and clas-sification of attack. International Conference on Advance in Intelligence Systems, Theory and Appli-cations, Luxembourg, Kirchberg, Luxembourg, IEEE, 148-153.
Nanthakumar, L., & Ibrahim, Y. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins SARIMA application. South Asian Journal of Tourism and Heritage, 3(2), 50-60.
Nochai, R., & Nochai, T. (2006). ARIMA model for forecasting oil palm price. Proceedings of 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Universiti Sains Malaysia, Penang, June 13-15, 1-7.
Nze, O.N. (2011). Assessment of the Productivity of the Nigerian Shipping Industry. An unpublished M.Sc. thesis, submitted to the Transport Management Technology Department, Federal University of Technology, Owerri.
Odularu, G.O. (2008). Crude oil and the Nigerian economic performance. Oil and Gas Business, 1-27.
Phusavat, K., & Aneksitthisin, E. (2000). Interrelationship among profitability, productivity and price recovery: Lessons learned from a wood-furniture company. In Proceedings of Industrial Engineering Network, Petchaburi, Thailand.
Paul, J.B., Hoque, M.S., & Rahman, M.M. (2013). Selection of best ARIMA model for forecasting av-erage daily share price index of pharmaceutical companies in Bangladesh: A case study on square pharmaceutical Ltd. Global Journal of Management and Business Research Finance, 13(3), 14 -26.
Rastegari, S., Saripan, M.I., & Rasid, M.F.A. (2009). Detection of denial of service attacks against do-main name system using neural networks. International Journal of Computer Science Issues, 6(1), 23-27.
Sarpong, S.A. (2013). Modelling and forecasting maternal mortality: An application of ARIMA mod-els. International Journal of Applied Science and Technology, 3(1), 19-28.
Sowell, F. (1992). Modelling long-run behaviour with the fractional ARIMA model. Journal of Mone-tary Economics, 29, 277-302.
Stephens, M.S., Stephens, O., Nze, O., Ibe, C.C., & Ukpere, W.I. (2012). An assessment of the produc-tivity of the Nigerian shipping industry using Saari productivity model. African Journal of Business Management, 6(15), 5414-5432.
Stanivuk, T., & Tokic, T. (2012). How to predict cargo handling times at the sea port affected by weather conditions. Croatian Operational Research Review (CRORR), 3, 103-112.
Tse, R.Y.C. (1997). An application of the ARIMA model to real-estate prices in Hong Kong. Journal of Property Finance, 8(2), 152-163.
World port source (WPS). Home site. Retrieved September 26, 2014, from http//:www.worldportsource.com.
Wu, J., Zhang, G., Zhang, Q., Zhou, J., & Wang, Y. (2011). Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter. Energy and Buildings, 43, 1685-1693.
Zemguliene, J. (2009). Productivity in the service sector: A service classification scheme for productivi-ty measurement. Ekonomika, 86, 81-88.
Zepka, G.d-S., Pinto Jr., O, Farias, W.R.G., Carretero, M.A., & Carneiro, J.C. (2008). A forecast cloud-to-ground lightning system based on neural network-preliminary results. 20th International Lightning Detection Conference, 21-23 April, Tucson, Arizona, USA.
Anyaeche, C.O., & Ighravwe, D.E. (2013). Predicting performance measures using linear regression and neural network: A comparison. African Journal of Engineering Research, 1(3), 84-89.
Bhardwaj, S.P. (2015). Market intelligence and price forecast. Retrieved April, 12, 2015, from www.iasri.res.in/cbp/data.
Blackard, J.A., & Dean, D.J. (1999). Comparative accuracies of artificial neural networks and discrimi-nant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24, 131-151.
Bretas, A.S., & Phadke, A.G. (2003). Artificial neural networks in power system restoration. IEEE Transactions on Power Delivery, 18(4), 1181-1186.
Cao, Q., & Schniederjans, M.J. (2006). Agent-mediated architecture for reputation-based electronic tourism systems: A neural network approach. Information & Management, 43, 598-606.
Contreras, J., Espinola, R., Nogales, F.J., & Conejo, A.J. (2003). ARIMA Models to Predict next-Day Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014-1020.
Engelbrecht, A.P. (2007). Computational Intelligence: An Introduction. John Wiley and Son Ltd.
Felipe, I.J.d-S, Mol, A.L.R., & e-Almeida, V.d-S. (2012). Application of ARIMA models in soybean series of prices in the north of Parana. Custos e @gronegócio on line, 8, 78-91.
Hakimpoor, H., & Arshad, K.A.B. (2011). Artificial neural networks’ applications in management. World Applied Sciences Journal, 14 (7), 1008-1019.
Indraratna, B., Rujikiatkamjorn, C., Geng, X., Ameratunga, J., & Boyle, P. (2011). Performance and prediction of vacuum combined surcharge consolidation at port of Brisbane, Coastal and Marine Geotechnics: Foundations for Trade, Sydney: Australia, 45-60.
Kamruzzaman, J., Sarker, R.A., & Begg, R. (2006). Artificial neural networks: Applications in finance and manufacturing, In Kamruzzaman J., Begg R. and Sarker R.A. (Eds.). Artificial neural networks: Applications in finance and manufacturing, Idea Group publishing, London.
Kamruzzaman, J., & Sarker, R.A. (2003). Comparing ANN based models with ARIMA for prediction of forex rates. ASOR Bulletin, 22(2), 1-10.
Kumar, Y., Das, B., & Sharma, J. (2011). Application of ANN in service restoration in distribution sys-tems with noisy input. IPCSIT, 7, 247-252.
Lee, M.H., Abd. Rahman, N.H., Suhartono, Latif, M.T., Nor, M.E., & Kamisan N.A.B. (2012). Sea-sonal ARIMA for forecasting air pollution index: A Case Study. American Journal of Applied Sci-ences, 9(4), 570-578.
Li, X., & Moller, M. (2009). Applying GLM model and ARIMA model to the analysis of monthly tem-perature of Stockholm. Department of Economics and Society, Dalarna University, 1-24.
Merh, N., Saxena, V.P., & Pardasani K.R. (2010). A comparison between hybrid approaches of ANN and ARIMA for Indian stock trend forecasting. Business Intelligence Journal, 3(2), 23 - 43.
Meyler, A., Kenny, G., & Quinn, T. (1998). Forecasting Irish inflation using ARIMA models economic Analysis. Research And Publications Department, Central Bank of Ireland.
Moradi, M., & Zulkernine, M., (2004). A neural network based system for intrusion detection and clas-sification of attack. International Conference on Advance in Intelligence Systems, Theory and Appli-cations, Luxembourg, Kirchberg, Luxembourg, IEEE, 148-153.
Nanthakumar, L., & Ibrahim, Y. (2010). Forecasting international tourism demand in Malaysia using Box Jenkins SARIMA application. South Asian Journal of Tourism and Heritage, 3(2), 50-60.
Nochai, R., & Nochai, T. (2006). ARIMA model for forecasting oil palm price. Proceedings of 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Universiti Sains Malaysia, Penang, June 13-15, 1-7.
Nze, O.N. (2011). Assessment of the Productivity of the Nigerian Shipping Industry. An unpublished M.Sc. thesis, submitted to the Transport Management Technology Department, Federal University of Technology, Owerri.
Odularu, G.O. (2008). Crude oil and the Nigerian economic performance. Oil and Gas Business, 1-27.
Phusavat, K., & Aneksitthisin, E. (2000). Interrelationship among profitability, productivity and price recovery: Lessons learned from a wood-furniture company. In Proceedings of Industrial Engineering Network, Petchaburi, Thailand.
Paul, J.B., Hoque, M.S., & Rahman, M.M. (2013). Selection of best ARIMA model for forecasting av-erage daily share price index of pharmaceutical companies in Bangladesh: A case study on square pharmaceutical Ltd. Global Journal of Management and Business Research Finance, 13(3), 14 -26.
Rastegari, S., Saripan, M.I., & Rasid, M.F.A. (2009). Detection of denial of service attacks against do-main name system using neural networks. International Journal of Computer Science Issues, 6(1), 23-27.
Sarpong, S.A. (2013). Modelling and forecasting maternal mortality: An application of ARIMA mod-els. International Journal of Applied Science and Technology, 3(1), 19-28.
Sowell, F. (1992). Modelling long-run behaviour with the fractional ARIMA model. Journal of Mone-tary Economics, 29, 277-302.
Stephens, M.S., Stephens, O., Nze, O., Ibe, C.C., & Ukpere, W.I. (2012). An assessment of the produc-tivity of the Nigerian shipping industry using Saari productivity model. African Journal of Business Management, 6(15), 5414-5432.
Stanivuk, T., & Tokic, T. (2012). How to predict cargo handling times at the sea port affected by weather conditions. Croatian Operational Research Review (CRORR), 3, 103-112.
Tse, R.Y.C. (1997). An application of the ARIMA model to real-estate prices in Hong Kong. Journal of Property Finance, 8(2), 152-163.
World port source (WPS). Home site. Retrieved September 26, 2014, from http//:www.worldportsource.com.
Wu, J., Zhang, G., Zhang, Q., Zhou, J., & Wang, Y. (2011). Artificial neural network analysis of the performance characteristics of a reversibly used cooling tower under cross flow conditions for heat pump heating system in winter. Energy and Buildings, 43, 1685-1693.
Zemguliene, J. (2009). Productivity in the service sector: A service classification scheme for productivi-ty measurement. Ekonomika, 86, 81-88.
Zepka, G.d-S., Pinto Jr., O, Farias, W.R.G., Carretero, M.A., & Carneiro, J.C. (2008). A forecast cloud-to-ground lightning system based on neural network-preliminary results. 20th International Lightning Detection Conference, 21-23 April, Tucson, Arizona, USA.