Based on 250 estimated JNNs, which differ in selected variables, sample breaking point and varying parameters (number of hidden neurons, weight value of the context unit), the model adequacy indicators for each JNN are calculated for two periods: in-the-sample and out-of-sample. The research is conducted at the aggregate level of euro area countries in the period from January 1999 to January 2017. the most commonly used variables in previous researches. The variables used as inputs include labour market variable, financial variable, external factor and lagged inflation, i.e. #Historical currency rates seriesIn this paper, Jordan neural network (JNN), a special type of NNs is examined, because of its advantages in time series forecasting suitable for inflation forecasting. is not fulfilled, neural networks (NNs) should be used for forecasting. In times of pronounced nonlinearity of macroeconomic variables and in situations when variables are not normally distributed, i.e. 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. This dimension space provides the number of inputs to the SVR model, which affects the complexity and the training time decrease of the model. 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). Finally, we use SVR to build a model for prediction exchange rate between US dollar and VND. Next, we use the False nearest neighbors algorithm to find the embedding dimension space of the de-noise signal. After that, we synthesis the signal without highest oscillation IFM to reduce noise. Firstly, we use Empirical Mode Decomposition (EMD) of the HHT to decompose a signal into multi oscillation scales called Intrinsic Mode Function (IMF). 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |