3. Data, Science & AI

Performs data scaling and normalization using TD_ScaleFit and TD_ScaleTransform for machine learning model preparation.

Performs convolution operations for signal processing and filtering in data analysis.

Provides logistic regression implementation for binary and multinomial classification tasks in machine learning.

Provides a decision forest ensemble classifier for robust and accurate machine learning predictions.

Enables advanced column transformations and feature engineering for machine learning data preparation.

Analyzes time series data by decomposing it into seasonal, trend, and residual components for pattern identification.

Analyzes user sessions and tracks user journeys to provide behavioral insights for product optimization.

Performs advanced parameter estimation and optimization for UAF models, enhancing predictive accuracy and model performance.

Evaluates regression models with comprehensive metrics and diagnostics using TD_RegressionEvaluator.

Performs time series forecasting using ARIMA models to predict trends and seasonal patterns in historical data.

Provides signal detrending for baseline correction and trend removal in scientific data analysis.

Performs vector distance calculations to measure similarity between data points, essential for AI and machine learning applications.

Identifies direct lag relationships in time series data by isolating correlations while controlling for intermediate lags through partial auto-correlation analysis.

Executes ADF, KPSS, and PP tests to assess stationarity in time series data for analysis and modeling.

Reconstructs time-domain signals from frequency-domain data using the inverse Fast Fourier Transform algorithm, essential for signal analysis.

Performs signal correlation analysis to measure similarity and detect time delays, a fundamental technique in signal processing and data analysis.

Comprehensive analytics for Generalized Linear Models (GLMs) in regression and classification tasks.

Streams and transforms JSON data through sequential multi-agent pipelines for efficient AI-driven data processing.

Controls active database sessions by terminating problematic connections, managing runaway queries, and resolving blocking issues to optimize performance.

Performs comprehensive data profiling and quality assessment using Teradata ClearScape Analytics' descriptive statistics functions for data validation and analysis.

Performs auto-correlation analysis on time series data to identify dependencies and detect recurring patterns.

Automates selection and comparison of machine learning models to determine optimal forecasting performance.

Applies signal smoothing and noise reduction techniques to clean and process time-series data for enhanced analysis and modeling.

Provides a Support Vector Machine (SVM) implementation for linear and non-linear classification in machine learning applications.