Modeling Analysis And Prediction Of Multivariate Time Series

In the data era,research on time series data has continued to evolve.In recent years,the analysis and prediction of related multiple time series data,that is,multivariate time series data,has become increasingly significant.In this context,based on the idea of sequence-to-sequence learning model,this paper designs and implements a prediction model that can effectively integrate multivariate time series data.In actual social life,time series data is widely used in many fields.In most scenarios,the target sequence data often does not exist alone,but coexists with a large number of related time series data to form a specific scene data set.These data types are different,the relationships are intricately connected,and the data structure is complicated.How to effectively integrate these multivariate data,integrate all data information,and achieve excellent prediction results is an important direction of current research.Most of the existing methods are not applicable to the complex data of the actual scene,and there are few models that adopt the latest sequence-to-sequence learning.Therefore,this paper makes use of the advanced self-attention mechanism to comprehensively consider the characteristics of network,hierarchical and multivariate data in the actual scene,and establish a deep neural network model to predict multivariate data.The model can adaptively select relevant time series data for encoding and can capture long-term dependencies.In the experiment of the actual data set,the model has good performance on many indicators.The model in this paper is based on the classical sequence-to-sequence learning framework and is divided into three parts:representation,encoding and decoding prediction of time series data.In the representation section,the model uses embedded and hierarchical attention mechanisms to represent data in hierarchical and multiple types of data,maximizing the fusion of information about all relevant sequence data.In the encoding and decoding prediction part,the model uses the encoder to vectorize the data,and then decodes the encoded result with a decoder to generate prediction data.In this paper,LSTM and self-attention mechanism are used as the structure of the encoder and decoder.Experiments show different application scenarios of the two methods.The final model has excellent prediction results on real data sets,and is superior to many existing prediction models in MAE,RMSE,MAPE and other evaluation indicators.The universality of the model is also proved by experiments with multiple data sets.