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The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current paper improves the previous work of Coelho et al. [1],...
Forecasting has always been of interest. Whether one's field is finance, health or seismology, being able to predict future values based on previously gathered data proves to be invaluable when taking decisions concerning the future. In this paper, we research machine learning techniques for predictions on time series and choose the best models that fit our use case, Smart Farms, in which we distributedly...
In this paper, a new method is proposed for predicting the future evolution of a time series of spacecraft telemetry data. Because such a time series has usually a non-stationary trend, nonlinear functional relationship between inputs and outputs and other uncertainties, an appropriate prediction model for spacecraft data is needed to set up. To this end, we analyze the characteristics of the Probability...
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