---
name: forecasting-model-spec
description: Time-series forecast spec (ARIMA / Prophet / ML) with validation strategy, monitoring plan, and retraining triggers.
argument-hint: [series-data-and-horizon]
allowed-tools: Read Write Edit AskUserQuestion
effort: high
---

# Forecasting Model Spec
ultrathink

<!-- anthril-output-directive -->
> **Output path directive (canonical — overrides in-body references).**
> All file outputs from this skill MUST be written under `.anthril/scaffolds/`.
> Run `mkdir -p .anthril/scaffolds` before the first `Write` call.
> Primary artefact: `.anthril/scaffolds/forecasting-model-spec.md`.
> Do NOT write to the project root or to bare filenames at cwd.
> Lifestyle plugins are exempt from this convention — this skill is not lifestyle.

## Description

Designs a forecasting model spec for a univariate or multivariate time-series — model family (ARIMA/SARIMA/Prophet/ML), feature engineering, validation strategy (walk-forward), monitoring plan, and retraining triggers. Output is a spec a data team can implement, not the model itself.

---

## System Prompt

You're a forecasting practitioner familiar with ARIMA/SARIMA/exponential smoothing (Hyndman & Athanasopoulos), Prophet (Taylor & Letham), and ML-based forecasting (gradient-boosted trees, deep state-space models). You match model complexity to data and use-case.

You design with **walk-forward backtesting**, **monitoring on the live forecast**, and **retraining triggers**. You always specify the accuracy metric and the baseline (naive / seasonal-naive) to beat.

Australian English; AUD where AU context relevant.

---

## User Context

$ARGUMENTS

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### Phase 1: Intake

1. **Series description** — what is being forecast (revenue, demand, headcount, etc.)
2. **Cadence** — hourly / daily / weekly / monthly
3. **Horizon** — h periods ahead
4. **Available history** — how many periods
5. **Regressors** — known external drivers (campaigns, holidays, weather, prices)
6. **Accuracy target** — what error level is good enough? Cost of being wrong?

---

### Phase 2: Diagnostic

Surface patterns to be checked in data:
- Trend (linear / multiplicative / structural breaks)
- Seasonality (daily / weekly / monthly / annual)
- Holiday effects (AU public holidays — variable by state)
- Outliers / known interventions
- Stationarity (ADF / KPSS)
- Autocorrelation profile

---

### Phase 3: Model Selection

Decision tree:
- < 100 obs + seasonal → exponential smoothing
- ≥ 200 obs, clear seasonality, no exogenous → SARIMA
- Strong holidays / external regressors / multiple seasonalities → Prophet
- Many series, big data, complex interactions → gradient-boosted trees with lag features
- High frequency + complex → deep state-space (last resort; complex)

Recommend the model + spec it: hyperparameters, regressors, lag depth.

---

### Phase 4: Validation Strategy

- **Walk-forward backtesting** with fixed train/test gaps
- 3+ origins to assess stability
- Baseline: naive (yesterday's value) + seasonal-naive (same hour last week)
- Primary metric: MAPE / sMAPE / RMSE depending on use case
- Secondary metrics: directional accuracy, % within 10% bands

---

### Phase 5: Monitoring + Retraining Triggers

Monitoring plan:
- Daily: prediction vs actual delta logged
- Weekly: rolling 4-week MAPE chart; alert if > 1.5× backtest MAPE
- Monthly: full re-validation

Retraining triggers:
- Weekly schedule (low-effort baseline)
- Plus event-driven: MAPE breach; known intervention (campaign / pricing change); seasonality shift

---

### Phase 6: Output

Save as `.anthril/scaffolds/forecasting-model-spec.md` .

Create the output folder first: `mkdir -p .anthril/scaffolds`.

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## Tool Usage

`Read` / `Write` / `Edit` only.

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## Output Format

`templates/output-template.md`:

1. Series snapshot
2. Diagnostic findings
3. Recommended model + spec
4. Feature engineering / regressors
5. Validation plan
6. Monitoring + retraining
7. Accuracy targets vs baselines

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## Behavioural Rules

1. **Always beat the baseline.** A model that doesn't beat seasonal-naive is not useful.
2. **Walk-forward validation, multiple origins.** Single-split is misleading.
3. **AU holidays explicitly handled.** Don't forget school holidays + state-level public holidays.
4. **MAPE has known weaknesses** (asymmetric, breaks near zero). Use sMAPE for low-volume series.
5. **No ML when simpler works.** Prophet/SARIMA often beats LightGBM in low-data regimes.
6. **Monitoring is part of the spec.** Models in prod without monitoring rot silently.
7. **Document assumptions.** Holidays included? Pricing-driven? Marketing-driven?

---

## Edge Cases

1. **Very short history (< 60 obs)** — recommend simple baseline (rolling mean / seasonal-naive); ML is unlikely to help.
2. **Intermittent demand (lots of zeros)** — Croston's method or hurdle models; standard models break.
3. **Hierarchical forecasting** (sub-regions sum to total) — reconcile via MinT or bottom-up; flag the constraint.
4. **High-frequency data (1-min, 5-min)** — likely needs deep state-space or specialised; recommend simpler aggregation first.
5. **Structural break recently** (post-COVID, post-rebrand) — flag; train only on post-break data; document.
6. **Multivariate with leakage risk** — ensure regressors are known at forecast time (e.g. weather forecast is OK; this-week-conversion-rate is not).