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td-smoothing

Smooth time series data using Teradata's TD_SMOOTHING function and UAF.

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

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of noisy or irregular time series data by applying advanced signal smoothing and noise reduction techniques, ensuring cleaner data for analysis and modeling.

Core Features & Use Cases

  • Signal Smoothing: Applies various smoothing algorithms to reduce high-frequency noise.
  • Noise Reduction: Filters out unwanted variations to reveal underlying trends.
  • Data Regularization: Prepares data for downstream analysis by making it more consistent.
  • Use Case: Analyze IoT sensor data from thousands of devices to smooth out erratic readings and identify genuine patterns in equipment performance.

Quick Start

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

Frequently Asked Questions about td-smoothing

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

FAQPage Schema
How do I reduce noise in time series data using Teradata?▼

You can reduce noise in time series data by applying advanced signal smoothing techniques with Teradata's TD_SMOOTHING function. This function filters out unwanted high-frequency variations to reveal underlying trends for cleaner analysis.

What is signal smoothing and when do I need it for IoT sensor data?▼

Signal smoothing is a data processing technique that reduces high-frequency noise to reveal underlying trends in datasets. You need it for IoT sensor data to smooth out erratic readings and identify genuine equipment performance patterns.

Can I use the TD_SMOOTHING function for high-dimensional time series datasets?▼

Yes, TD_SMOOTHING supports scalable analysis of high-dimensional datasets using Teradata's Unbounded Array Framework (UAF). It processes extensive time series tables containing timestamps and values from thousands of devices.

What's the best way to prepare time series data for smoothing and regularization?▼

To prepare time series data for regularization, ensure your table contains a timestamp column and corresponding value columns. The smoothing function then makes the data more consistent for downstream analysis.

Does td-smoothing support parameter optimization for different signal processing methods?▼

Yes, the Skill supports various smoothing methods alongside parameter optimization. This flexibility allows you to tailor the noise reduction process to fit the specific characteristics of your time series data.

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