Abstract
AdaBoost is an Artificial Intelligence algorithm widely used in classification problems with outstanding results in low complexity models. In this article, the prediction of the COLCAP series is carried out through the AdaBoost.RT algorithm with self-adaptive \(\varphi \). Firstly, the COLCAP index time series is analyzed in order to verify its stationarity by the unit root test. Exogenous information is used based on five time series of financial character, which were selected after performing a grey relational analysis and principal component analysis. To find optimal values of the algorithm, the variation of each value was executed. The results show that it is possible to predict the COLCAP index through AdaBoost using 48 weak classifiers resulting in MAPE = 1.247% and RMSE = 17.87. With a less complex model that uses two weak apprentices the results were MAPE = 1.403% and RMSE = 22.56.
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Reyes Fajardo, L., Gaona Barrera, A. (2018). Application of the AdaBoost.RT Algorithm for the Prediction of the COLCAP Stock Index. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_17
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