To predict military spending in China based on ARIMA and artificial neural networks models

• Autorzy: Kuo K.-C. , Chang C.-Y. , Lin W.-C.

Warianty tytułu

PL

Przewidywania wydatków militarnych Chin na podstawie modeli ARIMA i sztucznych sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

This study takes the initiative to forecast China’s military spending based on autoregressive integrated moving average (ARIMA) models and artificial neural networks (ANNs) models. The mean absolute percentage error (MAPE) approach is applied to measure prediction accuracy. The results indicate that these single variable ARIMA models show higher accuracy and stability than those made by the single variable ANNs models across the four time periods, namely the short term (1 year), the medium term (3 years), the medium-long term (5 years), and the long term (10 years). As to multiple variable ANNs models, the prediction accuracy of each model with different variables has advantages in different time periods. The highest accuracy for the long term predictions among all of the multivariate models is made by ANN2 including China’s military spending and GDP. ANN3 including variables of China’s military spending, GDP, and inflation rates illustrates the most accurate prediction for the short term and medium-long term, while ANN4 including China’s military spending, GDP, inflation rates, and Taiwan’s military spending shows the highest accuracy for the medium term prediction. This concludes the contributions of this study.

PL

W artykule przedstawiono wyniki analizy dotyczącej przewidywanych wydatków Chin na militaria, opracowanej na podstawie modelu autoregresji (ang. ARIMA) oraz sztucznych sieci neuronowych (ANN). Dokładność predykcji oparta została na funkcji średniej wartości absolutnej procentowego uchybu. Badania wykazują, że model ARIMA ma wyższą dokładność i stabilność niż model oparty na ANN w odniesieniu do czterech, różnych okresów (1, 3, 5, 10 lat), przy czym dla ANN badanie wykonano dla czterech wartości dokładności predykcji.

Słowa kluczowe

EN

artificial neural networks; ANNs; autoregressive integrated moving average; ARIMA; forecasting; military spending

PL

sztuczne sieci neuronowe; autoregresja; przewidywanie; wydatki militarne

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 3b
• Strony: 176--181
• Opis fizyczny: Bibliogr. 51 poz., tab.

Twórcy

autor

Kuo K.-C.

• Department of International Trade, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei 111, Taiwan, R.O.C.

autor

Chang C.-Y.

• Department of International Business Administration, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei 111, Taiwan, R.O.C.

autor

Lin W.-C.

• Department of Financial Management, National Defense University, 70, Section 2, Central North Road, Beitou District, Taipei, 112, Taiwan, R.O.C.

Bibliografia

• [1] Zheng B., China’s ‘Peaceful Rise’ to Great Power Status, J. Foreign Affairs September-October 84(5), pp. 18-24, 2005..
• [2] Chen S. and Feffer J., China’s Military Spending: Soft Rise or Hard Threat? J. Asian Perspective 33(4), pp. 47-67, 2009..
• [3] Stockholm International Peace Research Institute, SIPRI yearbook, 2011. (http://www.sipri.org/).
• [4] Zhang G., Patuwo B. E., and Hu M. Y., Forecasting with Artificial Neural Networks: the State of the Art. J. International Journal of Forecasting 14, pp. 35-62, 1998..
• [5] Khashei M. and Bijari M. A., Novel Hybridization of Artificial Neural Networks and ARIMA Models for Time Series Forecasting. J. Applied Soft Computing 11, pp. 2664-2675, 2011..
• [6] Shahwan T. and Odening M., Computational Intelligence in Economics and Finance. Springer, Berlin Heidelberg, New York, pp. 63-74, 2007.
• [7] Faruk D. O., A Hybrid Neural Network and ARIMA Model for Water Quality Time Series Prediction. J Engineering Applications of Artificial Intelligence 23, pp. 586-594, 2010.
• [8] Diaz-Robles L. A., Ortega J. C., Fu J. S., Reed G. D., Chow J. C., Watson J. G., and Moncada-Herrera J. A., A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Particulate Matter in Urban Areas: The Case of Temuco, Chile. J. Atmospheric Environment 42, pp. 8331-9340, 2008.
• [9] Sanhueza P., Vargas C., and Mellado P., Impact of Air Pollution by Fine Particulate Matter (PM10) on Daily Mortality in Temuco, J. Chile. Revista Medica De Chile 134, pp. 754-761, 2005. PRZEGLĄD ELEKTROTECHNICZNY, ISSN 0033-2097, R. 89 NR 3b/2013 181
• [10] Pope C. A. and Dockery D. W., Health Effects of Fine Particulate Air Pollution: Lines That Connect. J. Air & Waste Management Association 56, pp. 709-742, 2006.
• [11] Church K. B. and Curram S. P., Forecasting Consumers’ Expenditure: A Comparison between Econometric and Neural Network Models, International J. of Forecasting 12(2), pp. 255-267, 1996.
• [12] Ansuj A. P., Camargo M. E., Radharamanan R., and Petry D. G., Sales Forecasting Using Time Series and Neural Networks. J. Computers and Industrial Eng. 31, pp. 421-424 , 1996.
• [13] Koutroumanidis T., Ioannou K., and Arabatzis G., Predicting Fuelwood Prices in Greece with the Use of ARIMA Models, Artificial Neural Networks and a Hybrid ARIMA–ANN Model, J. Energy Policy 37, pp. 3627-3634, 2009.
• [14] Prybutok V. R., Yi J. S., and Mitchell D., Comparison of Neural Network Models with ARIMA and Regression Models for Prediction of Houston’s Daily Maximum Ozone Concentrations. J. European Journal of Operational Research 122, pp. 31-40, 2000.
• [15] Gutierrez-Estrada J-C., De Pedro-Sanz E., LO´ Pez-Luque R., and Pulido-Calvo I., Comparison between Traditional Methods and Artificial Neural Networks for Ammonia Concentration Forecasting in an Eel (Anguila Anguilla L.) Intensive Rearing System. J. Aquacultural Engineering 31(3–4), pp. 183-203, 2004.
• [16] Box G. E. P. and Jenkins G. M., Time Series Analysis Forecasting And Control., Holden-Day, San Francisco, 1976.
• [17] Pisoni E., Farina M., Carnevale C., and Piroddi L., Forecasting Peak Air Pollution Levels Using NARX Models. J. Engineering Applications of Artificial Intelligence 22, pp. 593-602, 2009.
• [18] Cybenko G., Approximations by Super Positions of a Sigmoidal Function, J. Mathematics of Control, Signals, and Systems 2, pp. 303-314, 1989.
• [19] Hornik K., Stinnchcombe M., and White H., Multi-layer Feed Forward Networks Are Universal Approximators, J. Neural Networks, pp. 359-366, 1989.
• [20] Pe’rez P. and Reyes J., Prediction of Maximum of 24-h Average of PM10 Concentrations 30-h in Advance in Santiago, Chile. J. Atmospheric Environment 36, pp. 4555-4561, 2002.
• [21] Pe’rez P. and Reyes J., An Integratesd Neural Network Model for PM10 Forecasting. J. Atmospheric Environment 40, pp. 2845-2851, 2006.
• [22] Schlink U., Herbarth O., Richter M., Dorling S., Nunnari G., Cawley G., and Pelikan E.. Statistical Models to Assess the Health Effects and to Forecast Ground-level Ozone. J. Environmental Modelling & Software 21, pp. 547-558, 2006.
• [23] Slini T., Kaprara A., Karatzas K., and Moussiopoulos N., PM10 Forecasting for Thessaloniki, Greece. J. Environmental Modelling & Software 21, pp. 559-565, 2006.
• [24] Sofuoglu S. C., Sofuoglu A., Birgili S., and Tayfur G., Forecasting Ambient Air SO2 Concentrations Using Artificial Neural Networks. Energy Sources Part B-Economics Planning and Policy 1, pp. 127-136, 2006.
• [25] Sousa S. I. V., Martins F. G., Pereira M. C., and Alvim-Ferraz M. C. M., Prediction of Ozone Concentrations in Oporto City with Statistical Approaches. J. Chemosphere 64, pp. 1141-1149, 2006.
• [26] Thomas S. and Jacko R. B., Model for Forecasting Expressway Fine Particulate Matter and Carbon Monoxide Concentration: Application of Regression and Neural Network Models. J. of the Air & Waste Management Association 57, pp. 480-488, 2007.
• [27] Foster W. R., Collopy F., and Ungar L. H., Neural Network Forecasting of Short, Noisy Time Series. J. Computers and Chemical Engineering 16 (4), pp. 293-297, 1992.
• [28] Goyal P., Chan A. T., and Jaiswal N., Statistical Models for the Prediction of Respirable Suspended Particulate Matter in Urban Cities. J. Atmospheric Environment 40, pp. 2068-2077, 2006.
• [29] Li F. F., Li D. L., Wei Y. G., Ma D. K., Ding Q. S., Dissolved Oxygen Prediction in Apostichopus Japonicus Aquaculture Ponds by BP Neural Network and AR Model. J. Sensor Letters 8(1), pp. 95-101, 2010.
• [30] Taskaya T. and Casey M. C. A., Comparative Study of Autoregressive Neural Network Hybrids. J. Neural Networks 18, pp. 781-789, 2005.
• [31] Denton J. W., How Good Are Neural Networks for Causal Forecasting? The J. of Business Forecasting 14 (2), 17 (1995).
• [32] Joerding W., Economic Growth and Military Spending. J. of Development Economics 21, pp. 35-40, 1986.
• [33] Chowdhury A. R., A Causal Analysis of Military Spending and Economic Growth. J. of Conflict Resolution 35,pp. 80-97, 1991.
• [34] Khilji N. M. and Mahmood A., Military Expenditures and Economic Growth in Pakistan. J. The Pakistan Development Review 36(411), pp. 791-808, 1997.
• [35] Chang T., Fang W., Wen L. F., and Liu C., Military Spending, Economic Growth and Temporal Causality: Evidence from Taiwan and Mainland China, 1952–1995. J. Applied Economics 3(10), pp. 1289-1299, 2000.
• [36] Abu-Bader S. and Abu-Qarn A., Government Expenditures, Military Spending and Economic Growth: Causality Evidence from Egypt, Israel and Syria. J. of Policy Modeling 25, pp. 567-583, 2003.
• [37] Kollias C., Manolas G., and Paleologou S-M., Defence Expenditure and Economic Growth in the European Union: A Causality Analysis, J. of Policy Modeling 26, 553-569, 2004.
• [38] Lai C. N., Huang B. N., and Yang C. W., Military Spending and Economic Growth Across the Taiwan Straits: A Threshold Regression Model. J. Defence and Peace Economics 6(1), pp. 45-57, 2005.
• [39] Lee C. C. and Chang C., The Long-run Relationship between Defence Expenditures and GDP in Taiwan. J. Defence and Peace Economics 17(4), pp. 361-385, 2006.
• [40] Lee C. C. and Chen S. T., Non-linearity in the Defence Expenditure-economic Growth Relationship in Taiwan. J. Defence and Peace Economics 18(6), pp. 537-555, 2007.
• [41] Wanger N. and Brauer J., Using Dynamic Forecasting Genetic Programming (DFGP) to Forecast United States Gross Domestic Product (US GDP) with Military Expenditure as An Explanatory Variable, J. Defence and Peace Economics, October 18(5), pp. 451-466, 2007.
• [42] Andreou A. S. and Zombanakis G. A., A Neural Network Measurement of Relative Military Security: The Case of Greece and Cyprus. J. Defence and Peace Economics 12(4), pp. 303-324, 2001.
• [43] Andreou A. S. and Zombanakis G. A., Financial Versus Human Resources in the Greek-Turkish Arms Race: A Forecasting Investigation Using Artificial Neural Networks, MPRA paper No. 13892, posted 09, January, pp. 1-34, 2000.
• [44] Starr H., Hoole F. W., Hart J. A., and Freeman J. R., The Relationship between Defense Spending and Inflation. J. of Conflict Resolution 28, pp. 103-122, 1984.
• [45] Chan S., The Impact of Defense Spending on Economic Performance: a Survey of Evidence and Problems. J. Orbis 29, pp. 403-434, 1985.
• [46] Deger S. and Smith R., Military Expenditure and Growth in Less Developed Countries. J. of Conflict Resolution 27, pp. 335-353, 1983.
• [47] Mishra A. K. and Desai V. R., Drought Forecasting Using Stochastic Models. J. Stochastic Environmental Research Risk Aassessment 19, pp. 326-339, 2005.
• [48] Allende H., Moraga C., and Salas R., Artificial Neural Networks in Time Series Forecasting: a Comparative Analysis. J. Kybernetik 38, pp. 685-707, 2002.
• [49] Haykin S., Neural Networks, a Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ, USA., 1999.
• [50] Morgan N. and Bourlard H., Generalization and Parameter Estimation in Feedforward Nets: Some Experiments, in: D.S. Touretzky (Ed.), J. Advances in Neural Information Processing Systems 2, pp. 630-637, 1990.
• [51] Lewis C. D., Industrial and Business Forecasting Method. Butterworth, London, UK., 1982.