Examinando por Materia "Redes Neuronales Recurrentes (RNN)"

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    Forecasting Colombian Yield Curves & Rates With LSTM and Nelson-Siegel Models
    (Universidad EAFIT, 2024) Uribe Ramírez, Sebastián; Almonacid Hurtado, Paula María
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    Imputation method based on recurrent neural networks for the internet of things
    (Universidad EAFIT, 2018) Rodríguez Colina, Sebastián; Mejía Gutiérrez, Ricardo
    The Internet of Things (IoT) refers to the new technological paradigm in which sensors and common objects, like household appliances, connect to and interact through the Internet -- This new paradigm, and the use of Artificial Intelligence (AI) and modern data analysis techniques, powers the development of smart products and services; which promise to revolutionize the industry and humans way of living -- Nonetheless, there are plenty of issues that need to be solved in order to have reliable products and services based on the IoT -- Among others, the problem of missing data posses great threats to the applicability of AI and data analysis to IoT applications -- This manuscript shows an analysis of the missing data problem in the context of the IoT, as well as the current imputation methods proposed to solve the problem -- This analysis leads to the conclusion that current solutions are very limited when considering how broad the context of IoT applications may be -- Additionally, this manuscript exposes that there is not a common experimental set up in which the authors have tested their proposed imputation methods; moreover, the experiments found in the literature, lack reproducibility and do not carefully consider how the missing data problem may present in the IoT -- Consequently, the reader will find two proposals in this manuscript: i) an experimental set up to properly test imputation methods in the context of the IoT; and ii) an imputation method that is general enough as to be applied to several IoT scenarios -- The latter is based on Recurrent Neural Networks, a family of supervised learning methods which have excel at exploiting patterns in sequential data and intrinsic association between the variables of data