A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction

For example, combining ARIMA with various decomposition algorithms such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) for predicting complex time series; For example, using an improved ARMA model for stock market forecasting. However, the above models need to be built on the basis of stable sequence data, and usually require testing and preprocessing of the original data, which may lead to the loss of some hidden information, especially in big data samples, and this disadvantage is easily magnified. With the development of computer technology, intelligent models represented by artificial neural networks (ANNs) are gradually emerging. This type of model is good at handling incomplete, fuzzy, uncertain, or irregular data, and has a good fit to nonlinear relationships. Shallow neural networks represented by backpropagation neural networks (BPNN) and shallow machine learning represented by support vector machines (SVM) are also widely used in financial market prediction. However, shallow neural networks do not consider the temporal nature of data, and financial time series often have certain long-term dependencies. Therefore, recurrent neural networks (RNNs) with memory function have become the latest choice. The output of RNN at a certain moment can be used as input to feedback to neurons again, and this cascade structure is very suitable for time series data, which can preserve the dependency relationships in the data.