该【路灯监控系统中时序数据流的异常值检测研究 】是由【wz_198613】上传分享，文档一共【3】页，该文档可以免费在线阅读，需要了解更多关于【路灯监控系统中时序数据流的异常值检测研究 】的内容，可以使用淘豆网的站内搜索功能，选择自己适合的文档，以下文字是截取该文章内的部分文字，如需要获得完整电子版，请下载此文档到您的设备，方便您编辑和打印。路灯监控系统中时序数据流的异常值检测研究 Title: Anomaly Detection in Time Series Data Stream of Road Lighting Monitoring System Abstract: Road lighting monitoring systems are increasingly being deployed to ensure efficient and reliable lighting across road networks. These systems generate massive amounts of time series data, capturing the operational status of individual streetlights at regular intervals. Detecting anomalous behavior within this data stream is crucial for the effective management and maintenance of road lighting infrastructure. This paper investigates various anomaly detection techniques for identifying outliers and abnormal patterns in time series data streams from road lighting monitoring systems. 1. Introduction - Background and significance of road lighting monitoring systems - Importance of anomaly detection for effective infrastructure management - Aim and objectives of the research 2. Literature Review - Overview of road lighting monitoring systems and data collection methods - Anomaly detection techniques in time series analysis - Comparative study of existing anomaly detection algorithms 3. Methodology - Preprocessing of time series data (data cleaning, normalization, feature extraction) - Statistical anomaly detection methods (z-score, IQR, modified z-score) - Machine learning-based methods (clustering, classification, deep learning) - Evaluation metrics for anomaly detection performance 4. Anomaly Detection Techniques Statistical-based Anomaly Detection - Description of commonly used statistical methods - Advantages and limitations of each method - Implementation details and parameter selection Machine Learning-based Anomaly Detection - Overview of machine learning algorithms for anomaly detection - Selection of appropriate algorithms for time series data streams - Model training, evaluation, and selection Deep Learning-based Anomaly Detection - Introduction to deep learning architectures (., LSTM, CNN) - Preprocessing steps for deep learning on time series data - Implementation and evaluation of deep learning models 5. Experimental Evaluation - Dataset description and characteristics - Experimental setup and methodology - Comparative analysis of anomaly detection algorithms - Evaluation of detection accuracies, false positive rates, and computational costs 6. Results and Discussion - Comparison of detection performance across different algorithms - Identification of strengths and weaknesses of each approach - Insightful analysis of anomalies identified in the road lighting monitoring system 7. Conclusion - Summary of research findings - Contributions to the field of road lighting monitoring systems - Suggestions for future research and improvements in anomaly detection techniques 8. References Note: The outline provided above is just a suggestion to help structure your research paper. You can add or modify sections as per your requirements, research findings, and desired length. Ensure to include relevant citations and provide detailed explanations of the techniques and algorithms used in your study.
