Anomaly Detection

  1. Data Fundamentals for Anomaly Detection
    1. Understanding Data in Anomaly Detection Context
      1. Data Quality Requirements
        1. Data Volume Considerations
          1. Data Velocity and Real-Time Constraints
            1. Label Availability and Scarcity
            2. Data Types and Structures
              1. Univariate Data
                1. Single Variable Analysis
                  1. Distribution Properties
                  2. Multivariate Data
                    1. Multi-dimensional Relationships
                      1. Correlation Structures
                      2. Time-Series Data
                        1. Temporal Dependencies
                        2. Spatial Data
                          1. Geographic Information Systems
                            1. Spatial Relationships
                            2. Graph and Network Data
                              1. Node and Edge Properties
                                1. Structural Patterns
                                2. Mixed Data Types
                                  1. Categorical Variables
                                    1. Numerical Variables
                                      1. Text and Unstructured Data
                                    2. Feature Engineering for Anomaly Detection
                                      1. Feature Creation Strategies
                                        1. Domain Knowledge Integration
                                          1. Statistical Feature Construction
                                            1. Temporal Feature Engineering
                                              1. Spatial Feature Engineering
                                              2. Feature Selection Techniques
                                                1. Filter Methods
                                                  1. Correlation-Based Selection
                                                    1. Mutual Information
                                                      1. Chi-Square Tests
                                                      2. Wrapper Methods
                                                        1. Forward Selection
                                                          1. Backward Elimination
                                                            1. Recursive Feature Elimination
                                                            2. Embedded Methods
                                                              1. Regularization-Based Selection
                                                                1. Tree-Based Feature Importance
                                                              2. Dimensionality Reduction
                                                                1. Linear Methods
                                                                  1. Principal Component Analysis
                                                                    1. Linear Discriminant Analysis
                                                                      1. Factor Analysis
                                                                      2. Non-Linear Methods
                                                                        1. t-Distributed Stochastic Neighbor Embedding
                                                                          1. Uniform Manifold Approximation and Projection
                                                                            1. Kernel PCA
                                                                            2. Neural Network-Based Methods
                                                                              1. Autoencoder-Based Reduction
                                                                                1. Variational Autoencoders
                                                                            3. Data Preprocessing and Preparation
                                                                              1. Missing Data Handling
                                                                                1. Missing Data Patterns
                                                                                  1. Imputation Strategies
                                                                                    1. Mean and Median Imputation
                                                                                      1. Forward and Backward Fill
                                                                                        1. Interpolation Methods
                                                                                          1. Model-Based Imputation
                                                                                          2. Deletion Strategies
                                                                                            1. Listwise Deletion
                                                                                              1. Pairwise Deletion
                                                                                            2. Data Scaling and Normalization
                                                                                              1. Min-Max Scaling
                                                                                                1. Standardization
                                                                                                  1. Robust Scaling
                                                                                                    1. Unit Vector Scaling
                                                                                                    2. Data Transformation Techniques
                                                                                                      1. Log Transformation
                                                                                                        1. Box-Cox Transformation
                                                                                                          1. Yeo-Johnson Transformation
                                                                                                            1. Quantile Transformation
                                                                                                            2. Categorical Data Encoding
                                                                                                              1. One-Hot Encoding
                                                                                                                1. Label Encoding
                                                                                                                  1. Target Encoding
                                                                                                                    1. Binary Encoding
                                                                                                                    2. Handling Class Imbalance
                                                                                                                      1. Undersampling Techniques
                                                                                                                        1. Random Undersampling
                                                                                                                          1. Edited Nearest Neighbors
                                                                                                                          2. Oversampling Techniques
                                                                                                                            1. Random Oversampling
                                                                                                                              1. SMOTE
                                                                                                                                1. ADASYN
                                                                                                                                2. Hybrid Approaches

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                                                                                                                            1. Introduction to Anomaly Detection

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                                                                                                                            3. Statistical Methods for Anomaly Detection