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1 janvier 2021
- handle: 10670/1.jc905i
Ce document est lié à :
10.21640/ns.v13i26.2752
info:eu-repo/semantics/openAccess
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neural network autoregressive ARIMA univariate time series prediction glucose diabetic neural networks diabetes algorithms analysis modelsSujets proches
Box-Jenkins models Forecasting, Box-Jenkins Autoregressive integrated moving average ARIMA Models, Box-Jenkins AlgorismCiter ce document
Daniel Arturo Olivares-Vera et al., « Comparison of algorithms for the prediction of glucose levels in patients with diabetes », Nova Scientia, ID : 10670/1.jc905i
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: This work presents a comparison between two algorithms for the prediction of glucose levels in diabetic patients by using a univariate time series. The algorithms are applied to the history of fasting glucose levels to predict the five following values. The comparison is performed between 1) The Autoregressive Neural Networks (ARNN) and 2) The autoregressive integrated moving average (ARIMA) models. A total of 70 series are analyzed, and we show that the results obtained for the ARIMA model have error percentages higher than 25% of the predicted value to the expected value. In contrast, in 73% of the cases, the percentage error was less than 25% for the Autoregressive Neural Networks.