Research on Big Data Spatiotemporal Sequence Mining Method Based on Deep Learning
In response to the many challenges in spatiotemporal sequence mining tasks,this article proposes a Long Short Term Memory(LSTM)method based on L2 regularization,aiming to enhance the modeling ability of traditional LSTM models in long short time sequences.Firstly,the framework of big data spatiotemporal sequence mining methods was analyzed,including data input,data preprocessing,deep learning models,regularization optimization,and data mining results.Secondly,the basic principles of LSTM and regularization based LSTM optimization methods were introduced.Finally,validate and evaluate the proposed method using a dataset.The experimental results show that this method has significant advantages in spatiotemporal sequence mining tasks,providing an effective method for solving spatiotemporal sequence mining problems.
万方数据
维普
