td-arimaestimate

Estimate ARIMA model parameters for time series data using Teradata UAF.

7|Updated Dec 3, 2025

Search Tags:#time series #forecasting #statistical modeling #arima #teradata uaf #parameter estimation

Ready to run

npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-arimaestimate

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: td-arimaestimate
Source: https://github.com/teradata-labs/claude-cookbooks/tree/main/skills/analytics/td-arimaestimate
Command: npx skills add https://github.com/teradata-labs/claude-cookbooks --skill td-arimaestimate

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the complex process of identifying and estimating the optimal parameters for ARIMA (AutoRegressive Integrated Moving Average) models, crucial for accurate time series forecasting.

Core Features & Use Cases

Automated Parameter Estimation: Automatically determines the best (p, d, q) and seasonal (P, D, Q, s) orders for ARIMA models.
Model Diagnostics: Provides comprehensive analysis of model fit, residual diagnostics, and autocorrelation to ensure model adequacy.
Use Case: Forecast future sales figures by first using this Skill to build a robust ARIMA model based on historical sales data, ensuring the model accurately captures trends and seasonality.

Quick Start

Use the td-arimaestimate skill to analyze the time series table 'my_database.sales_data' with timestamp column 'sale_date' and value column 'units_sold'.

Frequently Asked Questions about td-arimaestimate

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

FAQPage Schema

How do I estimate ARIMA model parameters for time series forecasting in Teradata?▼

You can estimate ARIMA parameters in Teradata by using the TD_ARIMAESTIMATE function. It calculates optimal non-seasonal and seasonal orders while generating detailed fit statistics and residual diagnostics for model selection.

What is the difference between seasonal and non-seasonal ARIMA models in statistical modeling?▼

Seasonal ARIMA models include seasonal orders (P, D, Q, s) to capture repeating patterns at fixed intervals, while non-seasonal models use standard (p, d, q) orders for trend analysis. TD_ARIMAESTIMATE supports both configurations.

Does TD_ARIMAESTIMATE require a specific Teradata environment to run?▼

Yes, TD_ARIMAESTIMATE requires Teradata Vantage with the Unbounded Array Framework (UAF) enabled. You also need appropriate licensing to access UAF functions for time series statistical modeling.

Can I automate ARIMA order selection for time series data?▼

Yes, automated parameter estimation determines the best (p, d, q) and seasonal (P, D, Q, s) orders for ARIMA models. This streamlines identifying optimal statistical configurations for accurate time series forecasting.

What diagnostics are provided for ARIMA model adequacy checking?▼

The estimation provides comprehensive model fit statistics, residual diagnostics, and autocorrelation analysis. These outputs help validate model adequacy and ensure the ARIMA model accurately captures trends and seasonality.

How do I forecast future sales using historical time series data with ARIMA?▼

Build a robust ARIMA model on historical sales data by estimating parameters to capture trends and seasonality. Use the resulting diagnostics and fit statistics to validate the model before generating future forecasts.