13 research outputs found
An early warning method for agricultural products price spike based on artificial neural networks prediction
In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year
Learning and the monetary policy strategy of the European Central Bank
C1 - Refereed Journal Articl
Jump Neural Network for Real-Time Prediction of Glucose ConcentrationArtificial Neural Networks
Prediction of the future value of a variable is of central importance in a wide variety of fields, including
economy and finance, meteorology, informatics, and, last but not least important, medicine. For example,
in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood
should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in
the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence
of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction
purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming
from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm
that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous
glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient
himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then
testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that
prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time
anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic
alerts and the improvement of artificial pancreas performance