A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decomposition

TL;DR

This paper introduces a novel time series prediction model combining Sequential General VMD and spatial smoothing LSTM to improve sales volume forecasting accuracy by effectively extracting dynamic features from complex market data.

Contribution

It proposes a new hybrid model that integrates spatial smoothing and variational mode decomposition with LSTM for enhanced market sales prediction accuracy.

Findings

Achieves higher prediction accuracy than traditional methods.

Effectively captures seasonal and periodic market trends.

Improves feature extraction from complex sales data.

Abstract

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.