In this paper, the combination of the Hilbert-Huang Transform (HHT), Support Vector Regression (SVR) and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Vietnamese Dong. Firstly, we use Empirical Mode Decomposition (EMD) of the HHT to decompose a signal into multi oscillation scales called Intrinsic Mode Function (IMF). After that, we synthesis the signal without highest oscillation IFM to reduce noise. Next, we use the False nearest neighbors algorithm to find the embedding dimension space of the de-noise signal. Finally, we use SVR to build a model for prediction exchange rate between US dollar and VND. By using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from twelve (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the SVR model, which affects the complexity and the training time decrease of the model. Experimental results indicated that this method not only reduces complication of the model but also achieves higher accuracy prediction than the direct use of original data.