Anomaly Detection in Time Series: From Statistical Measures to DBSCAN clustering скачать в хорошем качестве

Anomaly Detection in Time Series: From Statistical Measures to DBSCAN clustering

Join us for an insightful exploration of anomaly detection methods at the recent SigAI Orange County chapter meetup. Our speaker, Anton Vasilescu, dives deep into the world of time series data, specifically focusing on voltage readings from sensors. This session provides an overview of various approaches to anomaly detection, showcasing the application of statistical techniques and machine learning algorithms to isolate and understand anomalies in data. Main Topics Discussed: Anomaly Detection Overview: Introduction to different methodologies for anomaly detection, setting the stage for a detailed discussion. Data Source and Characteristics: Discussion of the time series data from sensors capturing voltage data, including the data's specific nuances and patterns. Statistical Measures and Visualization: Deep dive into statistical methods, including mean, median, mode, histograms, and Kernel Density Estimation (KDE), to analyze and visualize the data. Anomaly Isolation Strategies: Exploration of various techniques to isolate anomalies, such as using standard deviation, Z-score, KDE, Mean Absolute Deviation, robust Z-score, and the DBSCAN algorithm. Real-Time Application and Code Demonstration: Practical demonstration of applying the discussed methods to sensor voltage data using Python libraries, providing a hands-on look at anomaly detection. Discussion and Q&A: Interactive session where attendees engaged with the speaker, asking questions and discussing the intricacies of the methods presented. Closing Remarks: Summary of the session's key takeaways emphasizing the importance of understanding and detecting anomalies in data. This meetup provided a comprehensive look into anomaly detection, offering attendees a blend of theoretical knowledge and practical application. Whether you're a data enthusiast or a seasoned professional, this presentation demystifies the complex world of anomaly detection in time series data, equipping you with the knowledge and tools to tackle real-world data challenges. Stay tuned for our next session and dive deeper into the ever-evolving field of artificial intelligence and machine learning.