Examinando por Materia "ARIMA"
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Ítem Comparación de modelos de series temporales ARIMA, SARIMAX y LSTM para la predicción del índice COLCAP(Universidad EAFIT, 2024) Osorio Aristizábal, David Santiago; Valencia Díaz, ÉdisonÍtem Methodological advances in artificial neural networks for time series forecasting(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2014-06-01) Cogollo, M. R.; Velasquez, J. D.; Universidad EAFIT. Escuela de Ciencias; Modelado MatemáticoObjective: The aim of this paper is to analyze the development of new forecasting models based on neural networks. Method: We used the systematic literature review method employing a manual search of papers published on new neural networks models in the time period 2000 to 2010. Results: Only 18 studies meet all the requirements of the inclusion criteria. Of these, only three proposals considered a neural networks model using a process different to the autoregressive. Conclusion: Although studies relating to the application of neural network models were frequently present, we find that the studies proposing new forecasting models based on neural networks with a theoretical support and a systematic procedure for the construction of model, were scarce in the time period 2000-2010. © 2012 IEEE.Ítem Pronóstico de la inflación colombiana : una aproximación desde los modelos machine learning(Universidad EAFIT, 2022) Loaiza Zapata, José Fernando; Londoño Sierra, Liz Jeanneth; Riascos Salas, Jaime AndrésThe objective of this paper is to forecast monthly Colombian inflation based on its macroeconomic determinants. 7 machine learning models are used: linear regression, SMV, Decision Trees, MLP, KNN, SVR and LSTM, and 1 conventional ARIMA model. The models with the best prognosis were the ARIMA and the LSTM. Although, the prediction of the LSTM can be improved by making an optimal architecture of the data since it manages to capture the drastic changes of the variables, it could even be improved if the behavior of each of the divisions that make up the basic basket is included.