A Combination Model Based on Sequential General Variational Mode Decomposition Method for Time Series Prediction
This work addresses accurate prediction of non-stationary financial time series for market economy makers and investors, representing an incremental improvement over existing decomposition methods.
The authors tackled financial time series prediction by constructing a new SGVMD-ARIMA combination model using a non-linear approach, applied to online store sales and Australian beer sales data. The results showed their model outperformed single models and linear combination models from the control group, with improved advantages over traditional decomposition prediction models within the prediction interval.
Accurate prediction of financial time series is a key concern for market economy makers and investors. The article selects online store sales and Australian beer sales as representatives of non-stationary, trending, and seasonal financial time series, and constructs a new SGVMD-ARIMA combination model in a non-linear combination way to predict financial time series. The ARIMA model, LSTM model, and other classic decomposition prediction models are used as control models to compare the accuracy of different models. The empirical results indicate that the constructed combination prediction model has universal advantages over the single prediction model and linear combination prediction model of the control group. Within the prediction interval, our proposed combination model has improved advantages over traditional decomposition prediction control group models.