td-hierarchical-clustering
Hierarchical clustering for nested data grouping
Teradata HNSW Vector Search
Approximate nearest neighbor search using HNSW algorithm
td-ifft
Inverse Fast Fourier Transform for time domain reconstruction
td-kmeans
K-means clustering for customer segmentation and data grouping
Teradata K-Nearest Neighbors Analytics
K-Nearest Neighbors for classification and regression
td-linear-regression
Linear regression analysis for continuous target prediction
td-logistic-regression
Logistic regression for binary and multinomial classification
td-movavg-forecast
Moving average based forecasting for smoothed predictions
td-naive-bayes
Naive Bayes classifier for probabilistic classification
td-ngram-splitter
N-gram generation for text preprocessing
td-npath
Path analysis for sequential event patterns
td-onehot-encoding
Categorical variable encoding using TD_OneHotEncodingFit and Transform
Teradata ONNX Model Scoring
Score data using ONNX models (Bring Your Own Model)
Teradata Ordinal Encoding Suite
Ordinal encoding for ordered categorical variables
td-outlier-detection
Outlier detection and handling using TD_OutlierFit
td-pacf
Partial auto-correlation analysis for direct lag relationships
Teradata PMML Model Scoring
Score data using PMML models (Bring Your Own Model)
td-portman
Ljung-Box portmanteau tests for model diagnostics and residual analysis
td-powerspec
Power spectrum analysis for frequency domain insights and periodicity detection
td-random-forest
Random forest ensemble classifier for high-accuracy classification
td-regression-evaluator
Comprehensive regression model evaluation using TD_RegressionEvaluator
td-resample
Signal resampling and interpolation for rate conversion
Teradata ROC Curve Analysis
ROC curve and AUC analysis for binary classification evaluation
td-scale-fit
Data scaling and normalization using TD_ScaleFit and TD_ScaleTransform
td-seasonal-decompose
Seasonal pattern decomposition and analysis
Teradata Sentiment Extraction
Sentiment analysis for text documents and sentences
td-sessionize
Session analysis and user journey tracking
Teradata SHAP Explainability
SHAP values for model explainability and feature importance
Teradata Silhouette Analysis
Silhouette coefficient for clustering quality evaluation
td-simple-impute
Missing value imputation using TD_SimpleImputeFit
td-smoothing
Signal smoothing and noise reduction techniques
Teradata SMOTE Oversampling
Synthetic Minority Oversampling for imbalanced datasets
td-stationarity-test
Statistical tests for time series stationarity (ADF, KPSS, PP tests)
td-svm
Support Vector Machine for linear and non-linear classification
td-tfidf
Term Frequency-Inverse Document Frequency for text analysis
td-train-test-split
Data splitting for model validation using TD_TrainTestSplit
Teradata Univariate Statistics
Comprehensive descriptive statistics for data exploration
td-vector-distance
Vector distance calculations for similarity analysis
Teradata Word Embeddings
Word and document embeddings for text analytics
Teradata XGBoost Analytics
XGBoost gradient boosting for classification and regression
Teradata Z-Test
Z-test for population mean comparison with known variance
control-sessions
Control active sessions by terminating problematic sessions, managing runaway queries, and handling blocking situations
manage-queues
Manage delayed request queues including workload, system, and utility queues to release blocked requests or abort unnecessary items
manage-workloads
**Autonomously create** filters, throttles, and classification rules to implement new workloads or manage workload lifecycle in response to changing operational needs
monitor-queries
Track query execution using real-time resources, analyze query bands, access query logs, and identify performance patterns across the Teradata system
monitor-resources
Monitor AMP processor load, system physical resources, and capacity using real-time resources to track system health and identify performance bottlenecks
monitor-sessions
Monitor active Teradata sessions using real-time resources, view SQL execution details, identify blocking issues, and optionally take control actions
monitor-workloads
Monitor workload definitions, distribution, and TASM statistics using real-time resources to understand classification effectiveness and workload performance
analyze-performance
Analyze system performance using throttle statistics, query logs, and resource metrics to identify bottlenecks and optimization opportunities
optimize-throttles
Analyze throttle behavior, recommend optimal configurations, and autonomously create/modify throttles to balance resource allocation and meet performance SLAs
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