Time Series Forecasting - Production Patterns

Scope note: This skill covers forecasting system construction and evaluation. It is not part of the LLM-build or LLM-training stack — route LLM lifecycle, prompting, or provider questions to ai-llm.

July 2026 posture: define a cutoff timestamp before modelling, start with strong baselines, prefer horizon-aware validation over IID thinking, treat known-future covariates explicitly, and verify fast-moving tooling against current official docs before recommending it.

This skill is the implementation guide for forecasting systems:

• timestamp integrity, frequency checks, and point-in-time feature design
• local, global/panel, and hierarchical forecasting workflows
• leakage-safe backtesting, horizon-wise evaluation, and business-loss alignment
• probabilistic forecasting, calibration, and interval quality
• time-series foundation models (TSFMs) and zero-shot benchmark patterns
• forecasting-specific handoff, fallback, and lineage requirements

Use this skill for forecasting depth. Use sibling skills for general data science, generic LLM strategy, or full production operations.

When To Use This Skill