Ensemble of temporal convolutional and long short-term memory neural networks apply to forecasting USDCOP exchange rate

Almonacid Hurtado, Paula María2021-07-122021http://hdl.handle.net/10784/29952This paper applies a neural network with ensemble of temporal convolutional network (TCN) and long short-term memory (LSTM) layers approach to forecast foreign exchange rates between the US dollar (USD) and Colombian Peso (COP) and obtain a better performance. This study provides evidence on the TCN and LSTM neural network model’s effectiveness and efficiency in forecasting temporal series. It should contribute positively to developing theory, methodology, and practice of using an artificial neural network to develop a forecasting model for financial temporal series.spaLSTMRed neuronalesPredicciónConvoluciónEnsemble of temporal convolutional and long short-term memory neural networks apply to forecasting USDCOP exchange ratemasterThesisinfo:eu-repo/semantics/openAccessREDES DE INFORMACIÓNAPRENDIZAJE AUTOMÁTICO (INTELIGENCIA ARTIFICIAL)INFORMACIÓN TECNOLÓGICATECNOLOGÍA - ASPECTOS ECONÓMICOSLSTMNeural NetworkTemporal ConvolutionalForecastingAcceso abierto2021-07-12Torres Marulanda, Juan Esteban006.32 T693