Nota sobre a previsão do Índice de Preços ao Consumidor usando redes neuronais artificiais
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Dec 1, 2010
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Resumo
Neste artigo se prognostica a variação porcentual do Índice de Preços ao Consumidor na Colômbia usando uma rede neuronal artificial. O modelo obtido, uma rede neuronal tipo perceptron multicamada, é capaz de capturar o ciclo sazonal presente nos dados usando somente os dados passados, como entradas a rede neuronal, e não é necessário incluir variáveis fictícias que representem tal componente estrutural. A precisão desta aproximação é melhor que a obtida por outros modelos apresentados na literatura, incluídos aqueles com suavizações exponenciais, aproximadas a modelos SARIMA e outras configurações de redes neuronais.
Keywords
prediction, non-linear models, macroeconomics, SARIMA, exponential smoothingprevisão, modelos não lineares, macro-economia, SARIMA, suavizado exponencialpredicción, modelos no lineales, macroeconomía, SARIMA, suavizado exponencial
References
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Akaike, H. (1973), Information theory and an extension of the maximum likelihood principle. In B. Petrov and F. Csaki (Eds.), 2nd International Symposium on Information Theory (pp. 267-281). Budapest: Akademia Kiado.
Anders, U. and Korn, O. (1999). Model selection in neural networks. Neural Networks, 12, 309-323.
Bahi, J. M.; Contassot-Vivier, S. and Sauget, M. (2009). An incremental learning algorithm for function approximation. Advances in Engineering Software, 40 (8), 725-730.
Binner, J. M.; Bissoondeeal, R. K.; Elger, T.; Gazely, A. M. and Mullineux, A. W. (2005). A comparison of linear forecasting models and neural networks: An application to Euro inflation and Euro Divisia. Applied Economics, 37 (6), 665-680.
Binner, J. M.; Elger, T.; Nilsson, B. and Tepper, J. A. (2004). Tools for non-linear time series forecasting in economics: an empirical comparison of regime switching vector autoregressive models and recurrent neural networks. Advances in Econometrics, 19, 71-91.
Predictable non-linearities in U.S. inflation. (2006). Economics Letters, 93 (3), 323-328.
Binner, J. M.; Jones, B.; Kendall, G.; Tepper, J. and Tino, P. (2006). Does money matter?: An artificial intelligence approach. Documento procedente de 9th Joint Conference on Information Sciences, JCIS 2006 CIEF-129.
Chen, X.; Racine, J. and Swanson, N. (2001). Semiparametric ARX neural network models with an application to forecasting inflation. IEEE Transactions on Neural Networks, 12, 674-683.
Clements, M. P.; Frances, P. H. and Swanson, N. R. (2004). Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20, 168-183.
Düzgün, R. (2010). Generalized regression neural networks for inflation forecasting. International Research Journal of Finance and Economics, 51, 59-70.
Fahlman S. E. and Lebiere C. (1990). The Cascade-Correlation learning architecture. Advances in Neural Information Processing Systems. 2, 524-532.
Fletcher, R. (1987). Practical methods of optimization. New York: Wiley-Interscience.
Friedman, J. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1-141.
Ghiassi, M.; Saidane, H. and Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21, 341-362.
Granger, C. and Teräsvirta, T. (1993). Modeling nonlinear economic relationships. Oxford: Oxford University Press.
Gungor, C. and Berk, A. (2006). Money supply and inflation relationship in the Turkish Economy. Journal of Applied Sciences, 6 (9), 2083-2087.
Haykin, S. (1999). Neural networks: a comprehensive foundation. New York: Pearson.
Hannan, E. and Quinn, B. (1979). The determination of the order of an autoregression. Journal of Royal Statistical Society, Series B, 41, 190-195.
Heravi, S.; Osborn, D. and Birchenhall, C. (2004). Linear versus neural network forecasts for european industrial production series. International Journal of Forecasting, 20, 435-446.
Igel, C. and Hüsken, M. (2000). Improving the RPROP learning algorithm. Documento procedente de Second International Symposium on Neural Computation, NC2000, ICSC Academic Press.
Kaastra, I. and Boyd, M. (1996). Designing a neural network for forecasting financial and economic series. Neurocomputing, 10, 215-236.
Kantz, H. and Schreiber, T. (1999). Non-linear time series analysis. Cambridge, UK: Cambridge University Press.
LeCun, Y.; Bottou, L.; Orr, G. B. and Muller, K.-R. (1998). Efficient backprop. En Neural Networks: Tricks of the Trade (pp. 5-50). s. l.: Springer Lecture Notes in Computer Sciences 1524.
Lehtokangas, M. (1999). Modelling with constructive backpropagation. Neural Networks, 12 (45), 707-716.
Masters, T. (1993). Practical neural network recipes in C++. New York: Academic Press.
Neural, novel and hybrid algorithms for time series prediction. (1995). New York: John Wiley and Sons.
McAdam, P. and McNelis, P. (2005). Forecasting inflation with thick models and neural networks. Economic Modelling, 22 (5), 848-867.
McNelis, P. D. (2002). Nonlinear Phillips curves in the Euro Area and USA?: Evidence from linear and neural network models. Proceedings of the International Joint Conference on Neural Networks, 3, 2521-2526.
Moshiri, S. and Cameron, N. (2000). Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19 (3), 201-217.
Moshiri, S.; Cameron, N. E. and Scuse, D. (1999). Static, dynamic and hybrid neural networks in forecasting inflation. Computational Economics, 14 (3), 219-235.
Nakamura, E. (2005). Inflation forecasting using a neural network. Economics Letters, 86 (3), 373-378.
Nelson, M.; Hill, T.; Remus, W. and O'Connor, M. (1999). Time series forecasting using neural networks: should the data be deseasonalized first? Journal of Forecasting, 18, 359-367.
Rodríguez, N. y Siado, P. (2003). Un pronóstico no paramétrico de la inflación colombiana. Revista Colombiana de Estadística, 26 (2), 89-128.
Riedmiller, M. (1994). Advanced supervised learning in multi-layer perceptrons: from backpropagation to adaptive learning algorithms. Computer Standards and Interfaces, 16, 265-278.
Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks, 86-591.
Santana, J. C. (2006). Predicción de series temporales con redes neuronales: una aplicación a la inflación colombiana. Revista Colombiana de Estadística, 29 (1), 77-92.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
Stock, J. H. and Watson, M. W. (1998). A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series. JBES, 14 (1), 11-30.
Forecasting inflation. (1999). Journal of Monetary Economics, 44, 293-335.
Swanson, N. and White, H. (1997a). Forecasting economic time series using adaptive versus nonadaptive and linear versus non-linear econometric models. International Journal of Forecasting, 13, 439-461.
A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks. (1997b). Review of Economics and Statistics, 39, 540-550.
Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208-218.
Tseng, F. M.; Tzeng, G. H.; Yu, H. C. and Yuan, B. J. C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems, 118, 9-19.
Van Djck, D. (1999). Smooth transition models: extensions and outlier robust inference. Tesis de PhD no publicada, Erasmus University, Rotterdam.
Velásquez, J. D.; Franco, C. J. y Olaya, Y. (2010). Predicción de los precios promedios mensuales de contratos despachados en la Bolsa de Energía de Colombia usando máquinas de vectores de soporte. Cuadernos de Administración, 23 (40), 321-337.
Velásquez, J. D.; Olaya, Y. y Franco, C. J. (2010). Predicción de series temporales usando máquinas de vectores de soporte. Ingeniare. Revista Chilena de Ingeniería, 18 (1), 64-75.
Weng, D. (2010). The consumer price index forecast based on ARIMA model. Proceedings of WASE International Conference on Information Engineering, 5571115, 307-310.
Wong, W. K.; Xia, M. and Chu, W.C. (2010). Adaptive neural network model for time-series forecasting. European Journal of Operational Research, 207 (2), 807-816.
Zhang, G. P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28 (12), 1183-1202.
Patuwo, B. and Hu, M. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, 35-62.
Zhang, G. P. and Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160 (2), 501-514.
Akaike, H. (1973), Information theory and an extension of the maximum likelihood principle. In B. Petrov and F. Csaki (Eds.), 2nd International Symposium on Information Theory (pp. 267-281). Budapest: Akademia Kiado.
Anders, U. and Korn, O. (1999). Model selection in neural networks. Neural Networks, 12, 309-323.
Bahi, J. M.; Contassot-Vivier, S. and Sauget, M. (2009). An incremental learning algorithm for function approximation. Advances in Engineering Software, 40 (8), 725-730.
Binner, J. M.; Bissoondeeal, R. K.; Elger, T.; Gazely, A. M. and Mullineux, A. W. (2005). A comparison of linear forecasting models and neural networks: An application to Euro inflation and Euro Divisia. Applied Economics, 37 (6), 665-680.
Binner, J. M.; Elger, T.; Nilsson, B. and Tepper, J. A. (2004). Tools for non-linear time series forecasting in economics: an empirical comparison of regime switching vector autoregressive models and recurrent neural networks. Advances in Econometrics, 19, 71-91.
Predictable non-linearities in U.S. inflation. (2006). Economics Letters, 93 (3), 323-328.
Binner, J. M.; Jones, B.; Kendall, G.; Tepper, J. and Tino, P. (2006). Does money matter?: An artificial intelligence approach. Documento procedente de 9th Joint Conference on Information Sciences, JCIS 2006 CIEF-129.
Chen, X.; Racine, J. and Swanson, N. (2001). Semiparametric ARX neural network models with an application to forecasting inflation. IEEE Transactions on Neural Networks, 12, 674-683.
Clements, M. P.; Frances, P. H. and Swanson, N. R. (2004). Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting, 20, 168-183.
Düzgün, R. (2010). Generalized regression neural networks for inflation forecasting. International Research Journal of Finance and Economics, 51, 59-70.
Fahlman S. E. and Lebiere C. (1990). The Cascade-Correlation learning architecture. Advances in Neural Information Processing Systems. 2, 524-532.
Fletcher, R. (1987). Practical methods of optimization. New York: Wiley-Interscience.
Friedman, J. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1-141.
Ghiassi, M.; Saidane, H. and Zimbra, D. K. (2005). A dynamic artificial neural network model for forecasting time series events. International Journal of Forecasting, 21, 341-362.
Granger, C. and Teräsvirta, T. (1993). Modeling nonlinear economic relationships. Oxford: Oxford University Press.
Gungor, C. and Berk, A. (2006). Money supply and inflation relationship in the Turkish Economy. Journal of Applied Sciences, 6 (9), 2083-2087.
Haykin, S. (1999). Neural networks: a comprehensive foundation. New York: Pearson.
Hannan, E. and Quinn, B. (1979). The determination of the order of an autoregression. Journal of Royal Statistical Society, Series B, 41, 190-195.
Heravi, S.; Osborn, D. and Birchenhall, C. (2004). Linear versus neural network forecasts for european industrial production series. International Journal of Forecasting, 20, 435-446.
Igel, C. and Hüsken, M. (2000). Improving the RPROP learning algorithm. Documento procedente de Second International Symposium on Neural Computation, NC2000, ICSC Academic Press.
Kaastra, I. and Boyd, M. (1996). Designing a neural network for forecasting financial and economic series. Neurocomputing, 10, 215-236.
Kantz, H. and Schreiber, T. (1999). Non-linear time series analysis. Cambridge, UK: Cambridge University Press.
LeCun, Y.; Bottou, L.; Orr, G. B. and Muller, K.-R. (1998). Efficient backprop. En Neural Networks: Tricks of the Trade (pp. 5-50). s. l.: Springer Lecture Notes in Computer Sciences 1524.
Lehtokangas, M. (1999). Modelling with constructive backpropagation. Neural Networks, 12 (45), 707-716.
Masters, T. (1993). Practical neural network recipes in C++. New York: Academic Press.
Neural, novel and hybrid algorithms for time series prediction. (1995). New York: John Wiley and Sons.
McAdam, P. and McNelis, P. (2005). Forecasting inflation with thick models and neural networks. Economic Modelling, 22 (5), 848-867.
McNelis, P. D. (2002). Nonlinear Phillips curves in the Euro Area and USA?: Evidence from linear and neural network models. Proceedings of the International Joint Conference on Neural Networks, 3, 2521-2526.
Moshiri, S. and Cameron, N. (2000). Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19 (3), 201-217.
Moshiri, S.; Cameron, N. E. and Scuse, D. (1999). Static, dynamic and hybrid neural networks in forecasting inflation. Computational Economics, 14 (3), 219-235.
Nakamura, E. (2005). Inflation forecasting using a neural network. Economics Letters, 86 (3), 373-378.
Nelson, M.; Hill, T.; Remus, W. and O'Connor, M. (1999). Time series forecasting using neural networks: should the data be deseasonalized first? Journal of Forecasting, 18, 359-367.
Rodríguez, N. y Siado, P. (2003). Un pronóstico no paramétrico de la inflación colombiana. Revista Colombiana de Estadística, 26 (2), 89-128.
Riedmiller, M. (1994). Advanced supervised learning in multi-layer perceptrons: from backpropagation to adaptive learning algorithms. Computer Standards and Interfaces, 16, 265-278.
Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks, 86-591.
Santana, J. C. (2006). Predicción de series temporales con redes neuronales: una aplicación a la inflación colombiana. Revista Colombiana de Estadística, 29 (1), 77-92.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
Stock, J. H. and Watson, M. W. (1998). A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series. JBES, 14 (1), 11-30.
Forecasting inflation. (1999). Journal of Monetary Economics, 44, 293-335.
Swanson, N. and White, H. (1997a). Forecasting economic time series using adaptive versus nonadaptive and linear versus non-linear econometric models. International Journal of Forecasting, 13, 439-461.
A model selection approach to real time macroeconomic forecasting using linear models and artificial neural networks. (1997b). Review of Economics and Statistics, 39, 540-550.
Teräsvirta, T. (1994). Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association, 89, 208-218.
Tseng, F. M.; Tzeng, G. H.; Yu, H. C. and Yuan, B. J. C. (2001). Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems, 118, 9-19.
Van Djck, D. (1999). Smooth transition models: extensions and outlier robust inference. Tesis de PhD no publicada, Erasmus University, Rotterdam.
Velásquez, J. D.; Franco, C. J. y Olaya, Y. (2010). Predicción de los precios promedios mensuales de contratos despachados en la Bolsa de Energía de Colombia usando máquinas de vectores de soporte. Cuadernos de Administración, 23 (40), 321-337.
Velásquez, J. D.; Olaya, Y. y Franco, C. J. (2010). Predicción de series temporales usando máquinas de vectores de soporte. Ingeniare. Revista Chilena de Ingeniería, 18 (1), 64-75.
Weng, D. (2010). The consumer price index forecast based on ARIMA model. Proceedings of WASE International Conference on Information Engineering, 5571115, 307-310.
Wong, W. K.; Xia, M. and Chu, W.C. (2010). Adaptive neural network model for time-series forecasting. European Journal of Operational Research, 207 (2), 807-816.
Zhang, G. P. (2001). An investigation of neural networks for linear time-series forecasting. Computers & Operations Research, 28 (12), 1183-1202.
Patuwo, B. and Hu, M. (1998). Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting, 14, 35-62.
Zhang, G. P. and Qi, M. (2005). Neural network forecasting for seasonal and trend time series. European Journal of Operational Research, 160 (2), 501-514.
Como Citar
Velásquez H., J. D., & Franco C, C. J. (2010). Nota sobre a previsão do Índice de Preços ao Consumidor usando redes neuronais artificiais. Cuadernos De Administración, 23(41). https://doi.org/10.11144/Javeriana.cao23-41.nspi
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