  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.
