COMPARISON OF DT& GBDT ALGORITHMS FOR PREDICTIVE MODELING OF CURRENCY EXCHANGE RATES

Maan Y. Anad Alsaleem, Safwan O. Hasoon

Abstract


Recently, many uses of artificial intelligence have appeared in the commercial field. Artificial intelligence allows computers to analyze very large amounts of information and data, reach logical conclusions on many important topics, and make difficult decisions, this will help consumers and businesses make better decisions to improve their lives, and it will also help startups and small companies achieve great long-term success. Currency exchange rates are important matters for both governments, companies, banks and consumers. The decision tree is one of the most widely artificial intelligence tools used in data mining. With the development of this field the decision tree and Gradient boosting decision tree are used to predicate through constructed intelligent predictive system based on it. These algorithms have been used in many stock market forecasting systems based on global market data. The Iraqi dinar exchange rates for the US dollar are affected in local markets, depending on the exchange rate of the Central Bank of Iraq and the features of that auction. The proposed system is used to predict the dollar exchange rates in the Iraq markets Depending on the daily auction data of the Central Bank of Iraq (CBI). The decision tree and Gradient boosting decision tree was trained and testing using dataset of three-year issued by the CBI and compare the performance of both algorithms and find the correlation between the data. (Runtime, accuracy and correlation) criteria are adopted to select the best methods. In system, the characteristic of artificial intelligence have been integrated with the characteristic of data mining to solve problems facing organization to use available data for decision making and multi-source data linking, to provide a unified and integrated view of organization data.


Keywords


Decision Trees; Gradient boosting decision tree; Correlation; Accuracy; Run times; exchange rates

Full Text:

PDF

References


Koncz, K., Hilovská, S. P. (2012). Application of Artificial Intelligence and Data Mining Techniques to Financial Markets. Acta všfs, 6, 62–76.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29 (5), 1189–1232. doi: http://doi.org/10.1214/aos/1013203451

Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4), 367–378. doi: http://doi.org/10.1016/s0167-9473(01)00065-2

Gupta, B., Rawat, A., Jain, A., Arora, A., Dhami, N. (2017). Analysis of Various Decision Tree Algorithms for Classification in Data Mining. International Journal of Computer Applications, 163 (8), 15–19. doi: http://doi.org/10.5120/ijca2017913660

Feng, Z., Xu, C., Tao, D. (2018). Historical Gradient Boosting Machine. EPiC Series in Computing, 55, 68–80. doi: http://doi.org/10.29007/2sdc

Anghel, A., Papandreou, N., Parnell, T., Palma, De. A., Pozidis, H. (2018). Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms. Available at: https://arxiv.org/abs/1809.04559

Mu, Y., Liu, X., Wang, L. (2018). A Pearson’s correlation coefficient based decision tree and its parallel implementation. Information Sciences, 435, 40–58. doi: http://doi.org/10.1016/j.ins.2017.12.059

Guolin, K., Qi, M. et. al. (2017). LightGBM: A highly efficient gradient boosting decision tree. NIPS, 3149–3157.

Frenkel, J. A.; Bilson, J. F. O., Marston, R. C. (Eds.) (1984). Tests of Monetary and Portfolio Balance Models of Exchange Rate Determination. Exchange Rate Theory and Practise. Chicago: University of Chicago Press, 239–260.

Gençay, R. (1999). Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules. Journal of International Economics, 47 (1), 91–107. doi: http://doi.org/10.1016/s0022-1996(98)00017-8

Béreau, S., Villavicencio, A. L., Mignon, V. (2010). Nonlinear adjustment of the real exchange rate towards its equilibrium value: A panel smooth transition error correction modelling. Economic Modelling, 27 (1), 404–416. doi: http://doi.org/10.1016/j.econmod.2009.10.007

Wong, W. K., Xia, M., Chu, W. C. (2010). Adaptive neural network model for time-series forecasting. European Journal of Operational Research, 207 (2), 807–816. doi: http://doi.org/10.1016/j.ejor.2010.05.022

Anders, U., Hann, T. H., Nakaheizadeh, G.; Weigend, A. S., Abu-Mustafa, Y., Refens, A. P. N. (Eds.) (1997). Testing for Nonlinearity with Neural Networks. Decision Technologies for Financial Engineering. Singapore: World Scientific.

Low, A. H. W., Muthuswamy, J.; Dunis, C. (Ed.) (1996). Information Flows in High Frequency Exchange Rates.., Forecasting Financial Markets. Exchange Rates and Asset Management. Chichester: John Wiley & Sons.

Lemeshko, O., Yevdokymenko, M., Anad Alsaleem, N. Y. (2018). Development of the tensor model of multipath qоe-routing in an infocommunication network with providing the required quality rating. Eastern-European Journal of Enterprise Technologies, 5 (2 (95)), 40–46. doi: http://doi.org/10.15587/1729-4061.2018.141989




DOI: http://dx.doi.org/10.21303/2461-4262.2020.001132

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 Maan Y. Anad Alsaleem, Safwan O. Hasoon

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)