Abstract:

Financial time series has always been a problem in time series forecasting due to its non-linear, non-stationary, and low signal-to-noise ratio. This paper proposes a VMD-ELM model based on variational modal decomposition, and uses the unique advantages of variational modal decomposition in digital signal decomposition to decompose financial time series data into several sub-modes, and then use the decomposed sub-modes as The input data of the extreme learning machine is trained. By comparing the EMD-ELM model, the autoregressive moving average (ARMA) process on the feedforward neural network (FFNN) and the West Texas Intermediate (WTI), the Canadian/US exchange rate (CANUS), and the US industrial production (IP) And the effect on the time series data of the Nasdaq 100 Volatility Index (VIX) of the Chicago Board Options Exchange. Based on average absolute error (MAE), average absolute percentage error (MAPE) and square root mean square (RMSE), the predicted analysis results prove that the method proposed in this paper has excellent performance on multiple data sets.

Key words:

Financial time series, Variational modal decomposition, Extreme learning machine