Spatio Temporal Series Prediction Model Combining 3D Convolution and ConvLSTM
TANG Haibo
LU Zhenyu
YANG Qiang
Abstract：Spatio temporal series prediction aims to predict the situation in the future for a period of time based on the spa-tio-temporal series data of historical observations.Spatio temporal series has complex spatio-temporal correlation,and common methods will lose the long-range dependence with the increase of prediction step size,leading to the greatly reduced prediction ac-curacy of the last few frames.In this paper,a hybrid model integrating 3D convolutional neural network and cyclic convolutional neural network is proposed to predict time-space series.3D convolutional neural network mainly captures long-range dependence and extracts global time-space characteristics of fixed length historical information.Cyclic convolution neural network is used to cap-ture short-range dependence,extract local spatio-temporal features between frames,and fuse the information of two modes by de-signing a gating unit.In addition,multi-scale input strategy is used to improve the clarity of the predicted image.The experiment shows that the prediction result of the hybrid model is better than that of common prediction models.The introduction of 3D convolu-tion module greatly improves the accuracy of multi-step prediction and reduces the prediction error.
Keywords：spatio temporal seriesprediction3D convolutionConvLSTM
Publication Date：2025-09-20
Online Publishing Date：2025-12-23(First online date of this platform, not the publication date of the document)
Pages：5( 2450-2454 )