Temporal Pattern Recognition.prompt

# Temporal Pattern Recognition

## Overview

Temporal pattern recognition using LLMs involves identifying recurring patterns, trends, cycles, and anomalies in time-ordered data. LLMs excel at this task because they can combine statistical analysis with contextual understanding — recognizing that a sales spike coincides with a holiday, or that a metric anomaly correlates with a deployment.

This skill covers techniques for detecting patterns in temporal data, classifying pattern types, building pattern libraries for reuse, and integrating pattern recognition into monitoring and alerting systems.

## Key Concepts

- **Pattern taxonomy** — Spikes, dips, level shifts, trend changes, seasonality changes, volatility clusters
- **Shapelet detection** — Identifying characteristic subsequences that define pattern types
- **Contextual annotation** — Enriching patterns with business context and known events
- **Pattern matching** — Comparing new data against known pattern templates
- **Change point detection** — Identifying moments when the underlying data distribution shifts
- **Multi-scale patterns** — Patterns visible at different time granularities (hourly, daily, weekly)

## Implementation Patterns