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
optimize-throttles
Analyze throttle behavior, recommend optimal configurations, and autonomously create/modify throttles to balance resource allocation and meet performance SLAs
td-acf
Auto-correlation analysis for time series dependency and pattern detection
Teradata ANOVA
Analysis of variance for comparing group means
td-dfft
Fourier transformation for frequency domain analysis
td-diff
Time series differencing for stationarity and trend removal
td-fft
Fast Fourier Transform for frequency domain analysis and spectral decomposition
td-arima-forecast
ARIMA-based time series forecasting for trend and seasonal predictions
Teradata F-Test
F-test for comparing variances between two groups
td-glm
Comprehensive Generalized Linear Model analytics for regression and classification
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-detrend
Signal detrending for baseline correction and trend removal
td-decision-tree
Decision tree classifier for categorical prediction and rule extraction
td-logistic-regression
Logistic regression for binary and multinomial classification
td-decision-forest
Decision forest ensemble classifier for robust predictions
td-data-profiling
Comprehensive data profiling and quality assessment using Teradata ClearScape Analytics descriptive statistics functions
td-movavg-forecast
Moving average based forecasting for smoothed predictions
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
tune-workloads
Analyze workload classification and **autonomously configure** classification rules, filters, and priorities to improve accuracy and meet business requirements
Teradata Sentiment Extraction
Sentiment analysis for text documents and sentences
td-vector-distance
Vector distance calculations for similarity analysis
Teradata SHAP Explainability
SHAP values for model explainability and feature importance
td-sessionize
Session analysis and user journey tracking
Teradata Word Embeddings
Word and document embeddings for text analytics
td-seasonal-decompose
Seasonal pattern decomposition and analysis
td-scale-fit
Data scaling and normalization using TD_ScaleFit and TD_ScaleTransform
Teradata ROC Curve Analysis
ROC curve and AUC analysis for binary classification evaluation
Teradata ONNX Model Scoring
Score data using ONNX models (Bring Your Own Model)
Teradata Ordinal Encoding Suite
Ordinal encoding for ordered categorical variables
td-resample
Signal resampling and interpolation for rate conversion
td-outlier-detection
Outlier detection and handling using TD_OutlierFit
td-regression-evaluator
Comprehensive regression model evaluation using TD_RegressionEvaluator
Teradata PMML Model Scoring
Score data using PMML models (Bring Your Own Model)
td-random-forest
Random forest ensemble classifier for high-accuracy classification
td-powerspec
Power spectrum analysis for frequency domain insights and periodicity detection
td-portman
Ljung-Box portmanteau tests for model diagnostics and residual analysis
td-pacf
Partial auto-correlation analysis for direct lag relationships
td-arimaestimate
ARIMA parameter estimation for seasonal and non-seasonal AR, MA, ARMA, and ARIMA models
td-attribution
Marketing attribution modeling and analysis
Teradata AutoARIMA
Automated ARIMA model selection and forecasting
td-change-point
Change point detection in time series for structural breaks
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