  In order to solve the problems such as difficult to extract effective
features and low accuracy of sales volume prediction caused by complex
relationships such as market sales volume in time series prediction, we
proposed a time series prediction method of market sales volume based on
Sequential General VMD and spatial smoothing Long short-term memory neural
network (SS-LSTM) combination model. Firstly, the spatial smoothing algorithm
is used to decompose and calculate the sample data of related industry sectors
affected by the linkage effect of market sectors, extracting modal features
containing information via Sequential General VMD on overall market and
specific price trends; Then, according to the background of different Market
data sets, LSTM network is used to model and predict the price of fundamental
data and modal characteristics. The experimental results of data prediction
with seasonal and periodic trends show that this method can achieve higher
price prediction accuracy and more accurate accuracy in specific market
contexts compared to traditional prediction methods Describe the changes in
market sales volume.
