skills.rest
teradata-labs
teradata-labsOFFICIAL
Explore all skills by this author →
WebsiteGitHub

td-plot

Generate time series visualizations and UAF SQL workflows with TD_PLOT.

7|Updated Dec 3, 2025
Search Tags:#time series#data analysis#sql#visualization#uaf#teradata#td_plot
One-click install
npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-plot
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: td-plot
Source: https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-plot
Command: npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-plot

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the complex process of analyzing and visualizing time series data, transforming raw data into actionable insights without requiring deep expertise in Teradata's Unbounded Array Framework (UAF).

Core Features & Use Cases

  • Time Series Visualization: Generates advanced plots and diagnostic visualizations for time-dependent data.
  • UAF Workflow Generation: Automatically creates optimized SQL workflows using Teradata's UAF for scalable time series analysis.
  • Data Structure Analysis: Identifies temporal columns, value columns, and data frequency to tailor the analysis.
  • Use Case: Analyze sensor data from IoT devices to identify trends, anomalies, and seasonal patterns for predictive maintenance.

Quick Start

Analyze time series table: my_database.sensor_readings with timestamp column and value columns.

Frequently Asked Questions about td-plot

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I visualize and diagnose time series data in Teradata SQL?▼

To visualize and diagnose time series data in Teradata SQL, use the TD_PLOT function within the Unbounded Array Framework (UAF). This generates production-ready SQL workflows and advanced diagnostic plots to analyze temporal structures and identify trends.

What is the best way to generate diagnostic plots for IoT sensor data?▼

Generating diagnostic plots for IoT sensor data is best handled by analyzing temporal columns and data frequency to identify trends, anomalies, and seasonal patterns. This approach tailors the visualization for predictive maintenance and scalable time series analysis.

Do I need UAF expertise to analyze temporal structure and generate SQL workflows?▼

You do not need deep expertise in the Unbounded Array Framework to analyze temporal structure. This Skill automates UAF parameter recommendations and creates optimized SQL workflows, transforming raw time series data into actionable insights.

Can I use TD_PLOT for scalable signal processing and economic forecasting?▼

You can use TD_PLOT for scalable signal processing and economic forecasting by generating advanced visualizations tailored to your data. It analyzes temporal structures and recommends UAF parameters to support diverse industry use cases.

What are the limitations of using UAF for time series visualization?▼

A limitation of using UAF for time series visualization is the requirement for a Teradata database environment to execute the generated SQL workflows. The Skill relies on Teradata's Unbounded Array Framework to process and plot the temporal data.

Related Skills

Explore More Data & Analytics Skills→
teradata-labsteradata-labs
7

td-parameter-estimation

Generate production-ready SQL for Teradata UAF parameter estimation.

Official
Advanced
hung-phanhung-phan
1

time-series-features

Generate classical time-series features like lags and rolling windows.

Community
Intermediate
teradata-labsteradata-labs
7

td-seasonal-decompose

Decompose time series data into trend, seasonal, and residual components using Teradata UAF.

Official
Advanced
JonathanMitchell1234JonathanMitchell1234

r-expert

Provide R guidance for statistical analysis, ggplot2 visualization, and caret machine learning.

Community
Advanced