Time Series Forecasting - Production Patterns Skill

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

Activate this skill when the user asks for:

building or reviewing a forecast model
choosing between local, global/panel, hierarchical, or foundation-model approaches
creating lag, rolling, calendar, or known-future covariate features
designing a rolling-origin backtest or fixing temporal leakage
selecting forecasting metrics by horizon, segment, or business cost
adding prediction intervals, quantiles, or conformal calibration
comparing Chronos-2, Chronos-Bolt, Toto, TimesFM, AutoGluon TimeSeries, MLForecast, skforecast, or classical baselines
defining forecast-specific fallback, lineage, and handoff requirements

Scope Boundaries

General EDA, tabular modelling, experiment design, or reusable DS workflow -> ai-ml-data-science
Deployment architecture, monitoring stack, release gates, incident playbooks -> ai-mlops
Generic LLM lifecycle, prompting, or provider selection -> ai-llm
RAG and search systems -> ai-rag

Keep this skill focused on forecast creation, validation, and forecasting-specific operational patterns.

ASCII Flow

forecast need
  |
  v
forecast contract
  target + horizon + cutoff + granularity + business loss + update cadence
  |
  v
time-series validation
  timestamp integrity + leakage checks + rolling-origin backtest
  |
  v
model pattern
  naive baseline -> local/classical -> feature ML -> global/panel -> TS foundation
  |
  v
forecast handoff
  point/interval forecasts + lineage + fallback + monitoring trigger

Quick Reference

| Task | Default Tooling / Pattern | When To Use | |------|---------------------------|-------------| | Timestamp integrity | pandas, Polars, statsmodels | Frequency checks, missing timestamps, DST/timezone cleanup | | Feature-based forecasting | LightGBM/XGBoost + MLForecast or skforecast | Covariate-rich, interpretable, scalable forecasts | | Classical local baseline | naive, seasonal naive, ETS/SARIMAX | Small data, interpretable baseline, low-covariate settings | | Global/panel forecasting | MLForecast, AutoGluon TimeSeries, NeuralForecast | Many related series, shared signal, unified training | | Hierarchical coherence | HierarchicalForecast | Need forecasts that add up across levels | | Probabilistic forecasts | quantile regression, conformal, distributional models | Risk-sensitive decisions, service-level planning | | TS foundation models | Chronos-2 / Chronos-Bolt (Amazon), Toto (Datadog), TimesFM, AutoGluon TSFM support | Zero-shot baselines, long horizons, sparse feature engineering; verify current releases | | Backtesting | rolling-origin / expanding windows | Forecast validation without leakage | | Forecast handoff | cutoff timestamp + lineage + fallback | Production-ready forecast packages |

Workflow

Define the forecast target, horizon, cutoff timestamp, granularity, and business-loss shape first.
Route generic DS workflow or full production-ops work to the adjacent skill when forecasting depth is not the main need.
Choose the baseline, feature pattern, and model family from the decision tree.
Run leakage-safe backtests, compare horizon-aware metrics, and add interval or calibration checks when decisions need uncertainty.
Verify fast-moving tooling and TSFM claims with the navigation and fact-checking sources before final recommendations.

Current-Facts Protocol For Current-Best-Tooling Recommendations

Use this protocol whenever the user asks for the best, latest, current, or still relevant forecasting tooling — TSFM releases, benchmark leaderboards, and package APIs move on a roughly quarterly cadence, so treat any specific version number or benchmark figure below as provisional pending a live check:

Start from data/sources.json.
Verify fast-moving facts with official docs or official repositories.
Separate stable guidance from volatile facts such as current model capabilities, package APIs, or hosted deployment options.
Recommend a default plus 1-2 alternatives, with tradeoffs by horizon, covariates, scale, and operational cost.

Use this protocol for questions such as:

"Should I use MLForecast, skforecast, or AutoGluon TimeSeries?"
"Should I use Chronos-2, Chronos-Bolt, or Toto as my zero-shot baseline?"
"What is the current MAPIE API for time-series conformal intervals?"
"Should I still be using Prophet or TBATS here?"

Decision Tree: Choose The Forecasting Pattern

Need to build or review a forecast:
    ├─ One series or a small local portfolio?
    │   ├─ Few covariates, interpretability first -> naive/seasonal naive + ETS/SARIMAX baseline
    │   └─ Nonlinear effects or richer covariates -> feature-based boosting
    │
    ├─ Many related series?
    │   ├─ Need one shared model with covariates -> global/panel forecasting
    │   └─ Need rollups to add up across levels -> hierarchical forecasting + reconciliation
    │
    ├─ Intermittent or zero-heavy demand?
    │   ├─ Very sparse and operationally simple -> Croston/SBA/ADIDA baseline
    │   └─ Need richer covariates or scale -> global boosting with zero-aware features
    │
    ├─ Need uncertainty, service levels, or inventory decisions?
    │   └─ Add quantiles, conformal intervals, or distributional forecasts
    │
    ├─ Long horizon or low-feature setup?
    │   ├─ Need strong zero-shot baseline -> TS foundation models
    │   └─ Need supervised optimization on many series -> global/deep forecasting
    │
    └─ Need production handoff?
        └─ Record cutoff timestamp, feature contract, fallback, lineage, and retraining trigger

Core Principles

1. Cutoff Timestamp Before Features

Define the exact prediction timestamp before building labels or features.
Every lag, rolling aggregate, calendar flag, and external signal must be justified relative to what is known at that cutoff.
Known-future covariates and future-unknown covariates must be treated differently.

2. Baselines First

Always compare against naive and seasonal-naive baselines.
Add ETS/SARIMAX or other local classical baselines when interpretability matters.
Candidate models do not earn deployment if they barely beat a simple baseline.

3. Horizon And Slice Evaluation

Report accuracy by horizon, not only a single global score.
Slice by segment, geography, SKU family, volume band, or any business-relevant cohort.
Prefer MASE/WAPE/MAE over MAPE when zeros or near-zeros exist.

4. Global/Panel Before Per-Series Complexity

When many related series exist, default to a shared global/panel approach before building separate bespoke models.
Use hierarchical reconciliation when forecasts must remain coherent across levels.
Promote complexity only when it earns accuracy, calibration, or operational simplicity.

5. Probabilistic When The Decision Is Risk-Sensitive

Use quantiles, conformal intervals, or full predictive distributions when downstream actions depend on uncertainty.
Evaluate both coverage and sharpness; wide intervals with nominal coverage are not automatically useful.
Reassess calibration after every retrain or major model change.

6. Forecasting-Specific Handoff

A forecast package is incomplete without cutoff timestamps, horizon definition, feature contract, metric definitions, fallback rules, and lineage metadata.
Keep forecasting-specific handoff guidance here; route full deployment architecture to ai-mlops.

Known Traps

Mixing future-known covariates and future-unknown covariates in the same feature path without documenting which values are actually available at forecast time.
Using one global backtest score to justify deployment when horizon-specific error behavior differs materially.
Using MAPE on zero-heavy, intermittent, or near-zero series and then comparing models on unstable percentages.
Building bespoke per-series models before testing a strong global or panel baseline on related series.
Ignoring hierarchy and coherence when downstream consumers expect rollups to add up.

Common Anti-Patterns

Reusing IID validation habits from generic tabular ML instead of rolling-origin or expanding-window evaluation.
Treating decomposition visuals as evidence of production signal without backtesting the actual decision horizon.
Using feature-rich models whose future covariates are unavailable or operationally too expensive to maintain.
Comparing TS foundation models to weak baselines and calling the result strategic proof.

Navigation: Core References

Data Integrity And Features

TS EDA Best Practices - Timestamp integrity, missingness, decomposition, and stability checks
Lag & Rolling Patterns - Leakage-safe lags, rolling windows, and calendar patterns
Global & Panel Forecasting Patterns - Shared models, panel schemas, known-future covariates, grouped evaluation

Model And Strategy Selection

Model Selection Guide - May 2026 model-family decision matrix
LightGBM TS Patterns - Feature-based/global boosting patterns, MLForecast/skforecast workflows
Multi-Step Forecasting Patterns - Direct, recursive, and seq2seq tradeoffs
Intermittent Demand Patterns - Sparse-demand baselines and zero-aware modelling

Validation, Uncertainty, And Advanced Forecasting

Backtesting Patterns - Rolling-origin evaluation, panel-aware backtests, and metric design
Probabilistic Forecasting - Quantiles, conformal methods, calibration, and scoring rules
Hierarchical Forecasting - Coherent forecasts and reconciliation methods
Time-Series Foundation Model Patterns - Chronos, TimesFM, zero-shot baselines, and TSFM usage
Anomaly Detection Patterns - Residual and interval-based anomaly workflows

Handoff And Forecast Operations

Forecast Governance Patterns - Cutoff timestamps, lineage, fallback rules, and forecast contracts
Production Deployment Patterns - Broader production controls; use when forecasting-specific guidance must connect to deployment

Templates

Data Preparation

TS EDA Template - Reproducible structure for timestamp and seasonality review
Resample & Fill Template - Resampling, gap rules, and fill policies

Feature And Model Design

Lag & Rolling Features - Leakage-safe feature specification
Calendar Features - Known-future business calendar and event feature spec
Forecast Model Template - Forecast package contract for local, global, or hierarchical models
Multi-Step Strategy - Direct, recursive, and seq2seq strategy contract

Evaluation And Uncertainty

Backtest Template - Rolling-origin or expanding-window evaluation spec
TS Metrics Template - Horizon, slice, business-loss, and probabilistic metric contract

Foundation Models

TS Foundation Model Template - Zero-shot TSFM benchmark and evaluation scaffold

Scripts

| Script | Purpose | |--------|---------| | scripts/ts_evaluator.py | Stdlib-only CLI: horizon-wise backtest metrics, probabilistic calibration check, and full Markdown evaluation report |

# Horizon-wise accuracy: MAE, RMSE, MAPE, skill score vs naive baseline
python scripts/ts_evaluator.py backtest --input data/sample-forecast-results.json

# Probabilistic calibration check for 50%, 80%, 90% prediction intervals
python scripts/ts_evaluator.py calibration --input data/sample-forecast-results.json

# Full Markdown evaluation report written to file
python scripts/ts_evaluator.py report --input data/sample-forecast-results.json --output /tmp/ts-eval-report.md

Data Files

| File | Description | |------|-------------| | data/sources.json | Curated primary sources for classical forecasting, MLForecast, skforecast, AutoGluon, Chronos-2/Chronos-Bolt, TimesFM, Toto, fev-bench, and MAPIE | | data/sample-forecast-results.json | Realistic rolling-origin backtest results for a daily revenue forecast model across horizons 1, 7, 14, 30 days (48 rows, 12 backtest origins) |

External Sources

See data/sources.json for current primary sources across:

classical forecasting references
official docs for MLForecast, HierarchicalForecast, skforecast, AutoGluon TimeSeries, LightGBM, and MAPIE
official Chronos-2/Chronos-Bolt, TimesFM, and Toto repositories
fev-bench benchmark and governance references used for high-impact deployments

Related Skills

ai-ml-data-science - General DS workflows, experiment design, and broader modelling patterns
ai-mlops - Deployment architecture, monitoring, and release operations
ai-llm - Provider/model lifecycle questions outside time-series forecasting
data-sql-optimization - Storage and query design for time-series marts

Fact-Checking

Known bugs, regressions, framework/compiler/runtime footguns, and version-specific crash or workaround guidance must be verified against current primary web sources before being treated as current fact.
Verify volatile external facts, package APIs, hosted options, and model capabilities before final answers.
Prefer official docs, official repositories, standards, or release notes.
If web access is unavailable, state that clearly and mark time-sensitive guidance as unverified.

Learnings Loop

Before applying this skill on a non-trivial task, read learnings.consolidated.md in this directory (and learnings.md if present).

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.

Agent Skills: Time Series Forecasting - Production Patterns

Install this agent skill to your local

Skill Files