# Td Change Point

> Change point detection in time series for structural breaks

- Skill: `teradata-labs/td-change-point` (Agent Skill, multi-file: 13 files)
- Install (CLI): `npx skillmds@latest add teradata-labs/td-change-point`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teradata-labs/td-change-point/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: teradata-labs (https://skillmd.com/u/teradata-labs)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/teradata-labs/td-change-point

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# Teradata Change Point Detection

| **Skill Name** | Teradata Change Point Detection |
|----------------|--------------|
| **Description** | Change point detection in time series for structural breaks |
| **Category** | Time Series Analysis |
| **Function** | ChangePointDetection |
| **Framework** | Teradata Machine Learning Engine (MLE) |

## Core Capabilities

- **MLE table-operator implementation** with PARTITION BY and ORDER BY support
- **Scalable time series analysis** for millions of products or billions of IoT sensors
- **Multiple segmentation methods** including normal distribution modeling
- **Production-ready SQL generation** with proper MLE ON...USING syntax
- **Comprehensive error handling** and data validation
- **Business-focused interpretation** of analytical results
- **Flexible search methods** (binary, linear) for change point detection

## Machine Learning Engine (MLE) Overview

The Machine Learning Engine is Teradata's framework for advanced analytics using table operators:
- **Table-operator pattern**: `SELECT * FROM Function(ON table PARTITION BY ... ORDER BY ... USING ...)`
- **Parallel execution** across AMPs via PARTITION BY
- **Built-in functions** for classification, regression, time series, and more
- **SQL-native integration** with standard Teradata workflows

## Table Analysis Workflow

This skill automatically analyzes your time series data to generate optimized MLE workflows:

### 1. Time Series Structure Analysis
- **Temporal Column Detection**: Identifies time/date columns for ordering
- **Value Column Classification**: Distinguishes between numeric time series values
- **Series ID Detection**: Identifies grouping columns for PARTITION BY
- **Frequency Analysis**: Determines sampling frequency and intervals

### 2. MLE-Specific Recommendations
- **Partition Strategy**: Configures PARTITION BY for parallel processing
- **Parameter Optimization**: Suggests optimal parameters for ChangePointDetection
- **Search Method Selection**: Recommends binary vs linear search
- **Segmentation Method**: Suggests appropriate statistical model

### 3. SQL Generation Process
- **MLE Syntax Generation**: Creates proper ON...PARTITION BY...ORDER BY...USING SQL
- **Parameter Configuration**: Sets function-specific parameters
- **Result Storage**: Generates CREATE TABLE AS patterns for persisting results

## How to Use This Skill

1. **Provide Your Time Series Data**:
   ```
   "Analyze time series table: database.sensor_data with timestamp column and value columns"
   ```

2. **The Skill Will**:
   - Analyze temporal structure and sampling frequency
   - Identify optimal function parameters
   - Generate complete ChangePointDetection workflow
   - Provide performance optimization recommendations

## Input Requirements

### Data Requirements
- **Time series table**: Teradata table with temporal data
- **Time column**: Time/date column for ORDER BY
- **Value column**: Numeric column to analyze for change points
- **Series ID column**: Column to PARTITION BY (grouping key for parallel processing)

### Technical Requirements
- **Teradata Vantage** with Machine Learning Engine (MLE) enabled
- **MLE License**: Access to analytic table operators
- **Database permissions**: CREATE, DROP, SELECT on working database
- **Function access**: ChangePointDetection

## Output Formats

### Generated Results
- **Change point locations** with timestamps
- **Segment boundaries** identifying structural breaks
- **Statistical metrics** for each detected change point
- **Diagnostic information** for result validation

### SQL Scripts
- **Complete MLE workflows** ready for execution
- **Parameterized queries** optimized for your data structure
- **Result tables** with proper schema for downstream analysis

## Time Series Analysis Use Cases Supported

1. **Structural break detection**: Identify regime changes in time series
2. **Change point analysis**: Detect shifts in statistical properties
3. **Regime changes**: Find transitions between different data-generating processes
4. **Anomaly detection**: Locate abrupt changes in sensor or financial data

## Key Parameters for ChangePointDetection

- **TargetColumn**: The numeric column to analyze for change points
- **SegmentationMethod**: Statistical model for segments (e.g., 'normal_distribution')
- **SearchMethod**: Algorithm for finding change points ('binary' or 'linear')
- **MaxChangeNum**: Maximum number of change points to detect
- **Penalty**: Information criterion for model selection ('BIC', 'AIC', etc.)
- **OutputOption**: Type of output ('changepoint', 'segment', 'verbose')

## MLE Best Practices Applied

- **PARTITION BY optimization** for parallel processing across series
- **ORDER BY** with proper temporal column for time series ordering
- **Parameter tuning** specific to ChangePointDetection
- **Result persistence** using CREATE TABLE AS patterns
- **Error handling** for MLE-specific scenarios
- **Scalability considerations** for production workloads

## Example Usage

```sql
-- Example ChangePointDetection workflow
-- Replace parameters with your specific requirements

-- 1. Execute ChangePointDetection
SELECT * FROM ChangePointDetection (
    ON your_database.your_timeseries_table AS InputTable
    PARTITION BY series_id
    ORDER BY timestamp_col
    USING
    TargetColumn('value_col')
    SegmentationMethod('normal_distribution')
    SearchMethod('binary')
    MaxChangeNum(10)
    Penalty('BIC')
    OutputOption('changepoint')
) AS dt;
```

## Scripts Included

### Core MLE Scripts
- **`mle_data_preparation.sql`**: Data preparation for MLE processing
- **`td_change_point_workflow.sql`**: Complete ChangePointDetection implementation
- **`table_analysis.sql`**: Time series structure analysis
- **`parameter_optimization.sql`**: Function parameter tuning

### Integration Scripts
- **`mle_pipeline_template.sql`**: Multi-step analytical workflows
- **`performance_monitoring.sql`**: Execution monitoring
- **`result_interpretation.sql`**: Output analysis and visualization

## Industry Applications

### Supported Domains
- **Economic forecasting** and financial analysis
- **Sales forecasting** and demand planning
- **Manufacturing** process monitoring and quality control
- **IoT sensor data** analysis and alerting
- **Network monitoring** and traffic analysis
- **Energy** load and consumption pattern analysis

## Limitations and Considerations

- **MLE licensing**: Requires proper Teradata MLE licensing
- **Data ordering**: Time column must support meaningful ORDER BY
- **Computational complexity**: Large datasets with many partitions may be resource-intensive
- **Data quality**: Results depend on clean, well-structured time series data
- **Parameter sensitivity**: Function performance depends on proper parameter tuning
- **Temporal consistency**: Irregular sampling may affect detection quality

## Quality Checks

### Automated Validations
- **Time series structure** verification
- **Partition key** distribution checks
- **Parameter validation** for ChangePointDetection
- **Result quality** assessment

### Manual Review Points
- **Parameter selection** appropriateness
- **Result interpretation** accuracy
- **Performance optimization** opportunities
- **Integration** with existing workflows

## Updates and Maintenance

- **MLE compatibility**: Tested with latest Teradata Vantage releases
- **Performance optimization**: Regular MLE-specific optimizations
- **Best practices**: Updated with Teradata community recommendations
- **Documentation**: Maintained with latest MLE features

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*This skill provides production-ready time series change point detection using Teradata's Machine Learning Engine ChangePointDetection function with industry best practices for scalable analytics.*