Research And Application Of Time Series Data Anomaly Detection And Prediction Method Based On Deep Learning

With the continuous progress of technologies such as big data,the Internet of Things,and artificial intelligence,we are in an era of information explosion,where more and more time series data are recorded,containing important information at various moments.If we can discover the relationships between these time series,we can gain a deeper understanding of the world.At the same time,time series-related research as a sequence data analysis task has also received widespread attention.With the continuous progress of deep learning technology,rich data and mature models provide better performance for time series-related research.However,there are still some complications and limitations in existing research.The challenge of time series research lies in the high complexity and uncertainty of time series data,as well as the interaction and influence of multiple factors.In addition,time series data usually have high dimensions and long sequences,which increase the difficulty of model training and prediction,negatively affecting model performance.Therefore,this paper uses deep learning methods to study the problems of multi-dimensional time series anomaly detection and long-term prediction,to effectively deal with these problems and improve model performance.A long-term decomposition and prediction model,DMRformer,based on the Transformer has been proposed.To address the problem of difficulty in discovering temporal dependencies among time series from complex and unknown long-term patterns,as well as the insufficient predictive ability brought by the self-attention parallel structure of the Transformer,a progressive decomposition architecture based on the Transformer has been designed and improved to enhance the predictive performance.Specifically,the Transformer structure is used as a feature extractor to gradually decompose the changing trend of the hidden sequence throughout the entire prediction process,and the model also utilizes a multi-scale fusion residual attention mechanism to capture the long-term interdependent relationships of different sequence features,while reducing the consumption of computing resources,effectively achieving long sequence prediction.A multi-dimensional time series anomaly detection model based on the Transformer and GAN has been proposed.This model addresses the problems of difficulty in modeling temporal dependencies for multi-dimensional time series data and the increasing difficulty in effectively detecting anomalies due to the increase in data dimensions.Based on the DMRformer,this model integrates the GAN to amplify anomalies,and combines the DMRformer architecture to capture the temporal correlations in normal states and obtain more diverse time series reconstruction information.After training,the model detects anomalies through the discrimination results and reconstruction errors.By combining the above two deep learning models for time series,relying on relevant software development technologies and actual industrial scenarios,a time series data governance system for industrial Io T has been designed and implemented.The system mainly uses the proposed anomaly detection and prediction models to complete the prediction,anomaly detection,and related governance tasks of time series data in industrial scenarios.At the same time,the system also provides data integration management,data visualization,warning and alarm capabilities to facilitate operations and maintenance personnel to understand the on-site status of the work area in real-time and take corresponding measures in a timely manner when accidents occur.Through practical application verification,the system has a high degree of application feasibility.