Examinando por Materia "forecasting"

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    Ítem
    Forecasting stock return using a recurrent neural network apply to a financial optimization problem
    (Universidad EAFIT, 2021) Ochoa Ramírez, Juliana; Almonacid Hurtado, Paula Maria
    This paper presents a methodological proposal for optimizing financial asset portfolios by incorporating the returns predictions instead of the historical returns to calculate an efficient frontier. We changed the return means methodology to forecast by the return with LSTM neural network. We performed several simulation exercises to evaluate the methodology with real data from the US stock market to examine our portfolio optimization model. To evaluate our results, we compared the mean-variance frontier efficiency with the neural network return model. We selected one optimal portfolio that offered the highest expected return for a defined level of risk and compare both models. We show how the neural network return model has a better performance for different periods of time, outperforming the mean-variance model at the same level.
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    Ítem
    Implementation of system operation modes for health management and failure prognosis in cyber-physical systems
    (MDPI AG, 2020-01-01) Ruiz-Arenas, S.; Rusák, Z.; Mejía-Gutierrez, R.; Horváth, I.; Universidad EAFIT. Departamento de Ingeniería de Diseño; Ingeniería de Diseño (GRID)
    Cyber-physical systems (CPSs) have sophisticated control mechanisms that help achieve optimal system operations and services. These mechanisms, imply considering multiple signal inputs in parallel, to timely respond to varying working conditions. Despite the advantages that control mechanisms convey, they bring new challenges in terms of failure prevention. The compensatory action the control exerts cause a fault masking effect, hampering fault diagnosis. Likewise, the multiple information inputs CPSs have to process can affect the timely system response to faults. This article proposes a failure prognosis method, which combines time series-based forecasting methods with statistically based classification techniques in order to investigate system degradation and failure forming on system levels. This method utilizes a new approach based on the concept of the system operation mode (SOM) that offers a novel perspective for health management that allows monitoring the system behavior, through the frequency and duration of SOMs. Validation of this method was conducted by systematically injecting faults in a cyber-physical greenhouse testbed. The obtained results demonstrate that the degradation and fault forming process can be monitored by analyzing the changes of the frequency and duration of SOMs. These indicators made possible to estimate the time to failure caused by various failures in the conducted experiments. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
  • Miniatura
    Ítem
    Oracles in Pandemic: Forecasts of Colombian Economic Growth in 2020
    (Universidad EAFIT, 2021) Lozada González, Camilo Andrés; Perdomo Munévar, John Mauro; Torres Pinzón, Andrés Felipe