In Global Navigation Satellite Systems, the amplitude scintillation index ((S_4)) and the phase scintillation index ((\sigma_\phi)) indices are well-established indices used to monitor the scintillation activity. However, they are not able to distinguish the scintillation-induced signal from the Line-Of-Sight (LOS) dynamic. To circumvent this limitation, Kalman-based algorithms capable of separating the scintillation from the LOS signal have enabled the estimation of the scintillation signal directly from the postcorrelated data. A remaining challenge of both scientific significance and practical concern is to properly classify the estimated time series among different scenarios of scintillation intensity. In this article, a time series Convolutional Neural Network (CNN) is employed to classify the estimated amplitude and phase scintillation signal. The computer-generated scintillation data is based upon the Cornell Scintillation Model (CSM), where different values of decorrelation time and (S_4) index are used to build a multiclass dataset from which the CNN extracts the underneath features. Performance results, obtained via computer simulation, show that the proposed model can maintain a reasonable accuracy for certain levels of estimation noise, and can therefore provide a temporal characterization of the estimated scintillation signal.