Feature Analysis And Modeling For Multivariate Time Series

With the advent of the era of big data,research on time series data in various fields continues to grow.Time series data is essentially the realization of a random process,and time series is a series of random variables indexed by time.The time series forecasting uses various time series analysis methods to mine the internal laws of the time series and estimate the future data of the series.Predictive models with excellent effects can help researchers better analyze and make decisions,and reduce the negative effects of various unstable factors.Therefore,time series forecasting technology has developed rapidly and has become a hot research topic for decades.Time series forecasting models play an important role in explaining complex real-world elements.In recent years,with the rapid increase in the number and dimensions of data,how to extract the deep features of time series and identify potential patterns within the data has become a new difficulty and challenge in the field of time series forecasting.Deep learning is composed of multiple processing layers and can perform multiple levels of abstract learning.It has now become an effective method to overcome difficulties.However,time series data has complex characteristics,and it is difficult for a single deep learning model to explore the internal characteristics and external driving relationships of time series.For this reason,based on in-depth research on various deep learning technologies,this article proposes the following two time series feature modeling methods:1.An adaptive frequency domain modeling method for time series prediction is proposed.Research has found that time series forecasting relies on different frequency patterns,providing useful clues for future trend forecasting: short-term series forecasting relies more on high-frequency components,while long-term forecasting focuses more on low-frequency data.In order to better mine the multi-frequency mode of time series,this paper proposes an adaptive frequency domain modeling method for time series prediction,which is aimed at modeling the frequency domain information contained in the time series.The method is mainly divided into two stages: In the first stage,the model uses the XGBoost algorithm to measure the importance of the input vector and select high-importance features.In the second stage,the model integrates the frequency feature extraction of the time series and the frequency domain modeling of the target sequence,and proposes a novel prediction network based on the dependence of the time series on the frequency mode.The network can automatically pay attention to different frequency components according to the dynamic evolution of the input sequence,thereby revealing the multi-frequency pattern of the time sequence and strengthening the learning ability of the model.Finally,a large number of simulation experiments prove that the forecasting model designed in this paper has higher forecasting accuracy in time series forecasting,and it also has higher application value in actual analysis.2.A hierarchical attention network oriented to time series prediction is proposed.For an overly complex time series,simply capturing the time domain characteristics of the sequence cannot well identify deep latent patterns.To this end,this paper uses the encoder decoder architecture to design a hierarchical attention network.First,the encoder part uses convolutional neural network to learn the spatial interaction between different components of external source data,and aggregates external data into different levels of different information through multi-layer recurrent neural network,so as to make full use and modeling Its time dynamics.In the decoder stage,another multi-layer recurrent network is adopted,and operations are performed on different information at different levels through a multi-layer attention mechanism to select relevant information in the prediction.Experiments show that the proposed model can not only improve the accuracy of time series forecasting,but also capture sudden changes and oscillations in time series.