Finding Ghosts in Your Data
portes grátis
Finding Ghosts in Your Data
Anomaly Detection Techniques with Examples in Python
Feasel, Kevin
APress
11/2022
353
Mole
Inglês
9781484288696
15 a 20 dias
714
Descrição não disponível.
Part I. What is an Anomaly?.- Chapter 1. The Importance of Anomalies and Anomaly Detection.- Chapter 2. Humans are Pattern Matchers.- Chapter 3. Formalizing Anomaly Detection.- Part II. Building an Anomaly Detector.- Chapter 4. Laying out the Framework.- Chapter 5. Building a Test Suite.- Chapter 6. Implementing the First Methods.- Chapter 7. Extending the Ensemble.- Chapter 8. Visualize the Results.- Part III. Multivariate Anomaly Detection.- Chapter 9. Clustering and Anomalies.- Chapter 10. Connectivity-Based Outlier Factor (COF).- Chapter 11. Local Correlation Integral (LOCI).- Chapter 12. Copula-Based Outlier Detection (COPOD).- Part IV. Time Series Anomaly Detection.- Chapter 13. Time and Anomalies.- Chapter 14. Change Point Detection.- Chapter 15. An Introduction to Multi-Series Anomaly Detection.- Chapter 16. Standard Deviation of Differences (DIFFSTD).- Chapter 17. Symbolic Aggregate Approximation (SAX).- Part V. Stacking Up to the Competition.- Chapter 18. Configuring Azure Cognitive Services Anomaly Detector.- Chapter 19. Performing a Bake-Off.- Appendix: Bibliography.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Outlier Analysis;Anomaly Detection;Gestalt;Robust Statistics;Interquartile Range;Mahalanobis Distance;Changepoint Detection;Exponential Smoothing;Time Series Anomaly Detection;ARMA;ARIMA;Anomaly Detection as a Service;Anomaly Detection Principles and Algorithms;Anomaly Detection: Techniques and Applications;Python;Outlier and Anomaly Detection;Multivariate Anomaly Detection;Azure Cognitive Services Anomaly Detector
Part I. What is an Anomaly?.- Chapter 1. The Importance of Anomalies and Anomaly Detection.- Chapter 2. Humans are Pattern Matchers.- Chapter 3. Formalizing Anomaly Detection.- Part II. Building an Anomaly Detector.- Chapter 4. Laying out the Framework.- Chapter 5. Building a Test Suite.- Chapter 6. Implementing the First Methods.- Chapter 7. Extending the Ensemble.- Chapter 8. Visualize the Results.- Part III. Multivariate Anomaly Detection.- Chapter 9. Clustering and Anomalies.- Chapter 10. Connectivity-Based Outlier Factor (COF).- Chapter 11. Local Correlation Integral (LOCI).- Chapter 12. Copula-Based Outlier Detection (COPOD).- Part IV. Time Series Anomaly Detection.- Chapter 13. Time and Anomalies.- Chapter 14. Change Point Detection.- Chapter 15. An Introduction to Multi-Series Anomaly Detection.- Chapter 16. Standard Deviation of Differences (DIFFSTD).- Chapter 17. Symbolic Aggregate Approximation (SAX).- Part V. Stacking Up to the Competition.- Chapter 18. Configuring Azure Cognitive Services Anomaly Detector.- Chapter 19. Performing a Bake-Off.- Appendix: Bibliography.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Outlier Analysis;Anomaly Detection;Gestalt;Robust Statistics;Interquartile Range;Mahalanobis Distance;Changepoint Detection;Exponential Smoothing;Time Series Anomaly Detection;ARMA;ARIMA;Anomaly Detection as a Service;Anomaly Detection Principles and Algorithms;Anomaly Detection: Techniques and Applications;Python;Outlier and Anomaly Detection;Multivariate Anomaly Detection;Azure Cognitive Services Anomaly Detector
