High-resolution time-frequency (TF) analysis plays crucial role in characterizing multicomponent signal (MCSs) and estimating oscillatory properties. Linear time-frequency representations (TFRs) such as classical short-time Fourier transform (STFT) and continuous wavelet transform (CWT) incur constrained TF resolution and energy diffusion in both time and frequency direction. The synchrosqueezing transform (SST) represents a powerful sparse reassignment method that allows component reconstruction. This work introduces SST as extension to STFT and CWT and illustrates corresponding advantages of sharpening TFRs and recovery of instantaneous components. The SST effectiveness is assessed in practical situations that involve comparing STFT-based and CWT-based versions of synthetic data and also applying SST to optimize deep learning (DL) prediction model. It is demonstrated how SST achieves promising results in terms of improving TFR readability and increasing accuracy of DL-based prediction models.