Autoregressive Moving Average Recurrent Neural Networks Applied to the Modelling of Colombian Exchange Rate
dc.contributor.author | Sánchez-Sánchez, Paola Andrea | |
dc.contributor.author | García-González, José Rafael | |
dc.date.accessioned | 2018-12-05T16:27:12Z | |
dc.date.available | 2018-12-05T16:27:12Z | |
dc.date.issued | 2018 | |
dc.description.abstract | Modeling and prediction of time series has required in recent times a lot of attention, due to the necessity to have to make with accurate tools a right decision and to surpass theoretical, conceptual and practical limitations of the traditional approaches. In this sense, the neural networks have demonstrated to be a valuable tool, because they allow to represent nonlinear relationships, which are not well captured by other models. The investigations on neural networks have led to the development of different topologies, which adapt better to diverse problems. It is how, it seems to be that by the prediction problem, neural networks with some types of recurrence exhibit better approaches than other models, because they conserve a long memory of the series behavior. This paper proposes the use of autoregressive moving average recurrent neural networks (ARMA-NN) in the modeling and prediction of the Colombian exchange rate, evaluating its performance by the contrast with an autoregressive integrated moving average (ARIMA) model and a traditional feed-forward neural network (NN). ARIMA models and traditional feed-forward NN models are often compared with mixed conclusions in terms of the superiority in forecasting performance. In this paper, the results are in favor of the use of ARMA-NN models, every time that the prediction displays a better approach to the values of the series, which stimulates the use of such models in similar series and the research of other topologies of recurrence that allow better results. | spa |
dc.identifier.issn | 09740635 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12442/2384 | |
dc.language.iso | eng | eng |
dc.publisher | International Journal of Artificial Intelligence | eng |
dc.rights.accessrights | info:eu-repo/semantics/restrictedAccess | |
dc.rights.license | Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional | spa |
dc.source | International Journal of Artificial Intelligence | eng |
dc.source | Vol. 16, No. 2 (2018) | spa |
dc.source.uri | http://www.ceser.in/ceserp/index.php/ijai/article/view/5762 | eng |
dc.subject | Recurrent neural networks | eng |
dc.subject | Autoregressive moving average recurrent neural networks | eng |
dc.subject | Autoregressive integrated moving average models | eng |
dc.subject | Colombian exchange rate, time series, forecasting | eng |
dc.title | Autoregressive Moving Average Recurrent Neural Networks Applied to the Modelling of Colombian Exchange Rate | eng |
dc.type | article | eng |
dcterms.references | Arango L.E., González, A., Posada, C.E., 2002, Returns and Interest Rate: A Nonlinear Relationship in the Bogotá Stock Market. Appl. Finan. Econ. 12 (11), 835–842. | eng |
dcterms.references | Arango, L.E., Melo, L.F., 2006, Expansions and Contractions in Brazil, Colombia and Mexico: A view through nonlinear models. Journal of development economics 80 (2), 501-517. | eng |
dcterms.references | Berg, K., Mark, N., 2015, Third-country effects on the exchange rate. Journal of International Economics, 96 (2), 227-243. | eng |
dcterms.references | Bodyanskiy, Y., Popov, S., 2006, Neural network approach to forecasting of quasiperiodic financial time series. European Journal of Operational Research 175 (3), 1357–1366. | eng |
dcterms.references | Boero, G., Marrocu, E., 2002, The Performance of Non-linear Exchange Rate Models: A Forecasting Comparison. Journal of Forecasting 21 (7), 513-542. | eng |
dcterms.references | Box, G.E.P., Jenkins, G.M., 1970, Time Series Analysis, Forecasting and Control. Holden-Day, San Francisco, CA. | eng |
dcterms.references | Brock, W.A., Hsieh, D.A., LeBaron, B., 1991, Nonlinear dynamics, chaos, and instability. Cambridge, Mass.: MIT Press. | eng |
dcterms.references | Cao, L., Soofi, A.S., 1999, Nonlinear deterministic forecasting of daily dollar exchange rates. International Journal of Forecasting 15, 421-430. | eng |
dcterms.references | Clements, M.P., Smith, J., 2001, Evaluating forecasts from SETAR models of exchange rates. Journal of International Money and Finance 20, 133-148. | eng |
dcterms.references | Connor, J., Atlas, L.E., Martin, D.R., 1992, Recurrent networks and NARMA modeling. In Moody, J.E., Hanson, S.J. y Lippmann, R.P. editors, Advances in Neural Information Processing Systems, vol 4, 301–308. Morgan Kaufmann Publishers, Inc. | eng |
dcterms.references | De Gooijer, J.G., Ray, B.K., Krager, H., 1998, Forecasting exchange rates using TSMARS. Journal of International Money and Finance 17, 513-534. | eng |
dcterms.references | De Grauwe, P., Dewachter, H., Embrechts, M., 1993, Exchange rate theory. Chaotic models of foreign Exchange markets. Oxford UK: Blackwell. | eng |
dcterms.references | El Shazly, M., El Shazly, H.E., 1999, Forecasting currency prices using a genetically evolved neural network architecture. International Review of Financial Analysis 81, 67-82. | eng |
dcterms.references | Gencay, R., 1999, Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics 47, 91-107. | eng |
dcterms.references | Haykin, S., 1999, Neural Networks, a Comprehensive Foundation. Prentice Hall. | eng |
dcterms.references | Imbs J., Mumtaz, H., Ravn, M.O., Rey, H., 1996, Non linearities and Real Exchange Rate Dynamics. Journal of the European Economic Association 2003 I(2-3), 639-649. | eng |
dcterms.references | Ince, H., Trafalis, T.B., 2006, A hybrid model for exchange rate prediction. Decision Support Systems 42 (2), 1054-1062. | eng |
dcterms.references | Joelianto, E., Widiyantoro, S., Ichsan, M., 2009, Time series estimation on earthquake events using ANFIS with mapping function. International Journal of Artificial Intelligence 3, 37-63. | eng |
dcterms.references | Kaur, G. Dhar, J., Guha, R., 2014, Stock market forecasting using ANFIS with OWA operator. International Journal of Artificial Intelligence 12, 102-114. | eng |
dcterms.references | Kilian, L., Taylor, M. P., 2003, Why is it so difficult to beat the random walk forecast of exchange rates?. Journal of International Economics 60, 85-107. | eng |
dcterms.references | Kuan, C., Liu, T., 1995, Forecasting Exchange rates using feedforwad and recurrent neural networks. Journal of Applied Econometrics 10, 347-364. | eng |
dcterms.references | Leung, M., Chen, A.S., Daouk, H., 2000, Forecasting exchange rates using general regression neural networks. Computers & Operations Research 27, 1093-1110. | eng |
dcterms.references | Lubecke, T., Doo Nam, K., Markland, R., Kwok, C., 1998, Combining Foreign Exchange Rate Forecasts Using Neural Networks. Global Finance Journal 9 (l), 5-27. | eng |
dcterms.references | Majhi, R., Panda, G., Sahoo, G., 2009, Efficient prediction of exchange rates with low complexity artificial neural network models. Expert Systems with Applications 36 (1), 181-189. | eng |
dcterms.references | Mandic, D., Chambers, J.A., 2001, Recurrent Neural Networks for Prediction. Ed. John Wiley & sons. | eng |
dcterms.references | McMillan, D., 2005, Smooth-transition error-correction in exchange rates. North American Journal of Economics and Finance 16, 217-232. | eng |
dcterms.references | Medeiros, M.C., Veiga, A., Pedreira, C.E., 2001, Modeling exchange rates: smooth transitions, neural networks, andlinear models. Neural Networks, IEEE Transactions 12 (4), 755-764. | eng |
dcterms.references | Milas, C., Otero, J., 2002, Modelling oficial and parallel exchange rates in Colombia under alternative regimes: a non-linear approach. Economic Modelling 20, 165-179. | eng |
dcterms.references | Najand, M., Bond, C., 2000, Structural models of exchange rate Determination. Journal of Multinational Financial Management 10, 15-27. | eng |
dcterms.references | Ojeda-Joya, J.N., Sarmiento, G., 2018, Sovereign risk and the real exchange rate: A non-linear approach. International Economics, https://doi.org/10.1016/j.inteco.2017.05.003 | eng |
dcterms.references | Panda, Ch., Narasimhan, V., 2007, Forecasting exchange rate better with artificial neural network. Journal of Policy Modeling 29, 227-236. | eng |
dcterms.references | Qia, M., Wu, Y., 2003, Nonlinear prediction of exchange rates with monetary Fundamentals. Journal of Empirical Finance 10, 623-640. | eng |
dcterms.references | Revéiz, A., 2002, Evolution of the Colombian peso within the currency bands, nonlinear analysis and stochastic modeling. Rev. Econ. Ros. 5 (1), 37-91. | eng |
dcterms.references | Riedmiller, M., 1994, Advanced supervised learning in multi-layer perceptrons--from backpropagation to adaptive learning algorithms. Computer Standards and Interfaces 16, 265-278. | eng |
dcterms.references | Sánchez, P., García, J.R., 2017, A new methodology for neural network training ensures error reduction in time series forecasting. Journal of computer science 13 (7), 211-217. | eng |
dcterms.references | Sarantis, N., 1999, Modeling non-linearities in real effective exchange rates, Journal of International Money and Finance 18 (1), 27-45. | eng |
dcterms.references | Tenti, P., 1996, Forecasting Foreign Exchange Rates Using Recurrent Neural Networks. Applied Artificial Intelligence 10, 567-581. | eng |
dcterms.references | Tseng, F.M., Tzeng, G.H., Yu, H.C., Yuan, B.J.C., 2001, Fuzzy ARIMA model for forecasting the foreign exchange market. Fuzzy Sets and Systems 118, 9-19. | eng |
dcterms.references | Villa, F., Velásquez, J.D., Sánchez, P.A. 2015, Overfitting control inside cascade correlation neural networks applied to electricity contract price prediction. Rev. ing. univ. Medellín 14 (26), 161-176. | eng |
dcterms.references | Wang, L., Hu, M., 2013, Almost periodic solution of recurrent neural networks with backward shift operators on time scales. International Journal of Applied Mathematics & Statistics 47 (17), 78-86. | spa |
dcterms.references | Zhao, K., Gong, P., Wang, L., 2013, Exponential synchronization of a class of fuzzy recurrent neural networks with time-varying delays and impulses on time scales. International Journal of Applied Mathematics & Statistics 48 (18), 325-338. | eng |
Archivos
Bloque de licencias
1 - 1 de 1
No hay miniatura disponible
- Nombre:
- license.txt
- Tamaño:
- 368 B
- Formato:
- Item-specific license agreed upon to submission
- Descripción: