# Examples, Checklists, and Validation

Runnable examples, the pre-delivery quality checklist, the mistakes that most often
produce wrong forecasts, and the regression checks that confirm the skill still works.

## Examples

Three fully-working reference examples live in `examples/`. Use them as ground truth for correct API usage and expected output shape.

| Example | Directory | What It Demonstrates | When To Use It |
| ------- | --------- | -------------------- | -------------- |
| **Global Temperature Forecast** | `examples/global-temperature/` | Basic `model.forecast()` call, CSV -> PNG -> GIF pipeline, 36-month NOAA context | Starting point; copy-paste baseline for any univariate series |
| **Anomaly Detection** | `examples/anomaly-detection/` | Two-phase detection: linear detrend + Z-score on context, quantile PI on forecast; 2-panel viz | Any task requiring outlier detection on historical + forecasted data |
| **Covariates (XReg)** | `examples/covariates-forecasting/` | `forecast_with_covariates()` API (TimesFM 2.5), covariate decomposition, 2x2 shared-axis viz | Retail, energy, or any series with known exogenous drivers |

### Running the Examples

```bash
# Global temperature (no TimesFM 2.5 needed)
cd examples/global-temperature && python run_forecast.py && python visualize_forecast.py

# Anomaly detection (uses TimesFM 1.0)
cd examples/anomaly-detection && python detect_anomalies.py

# Covariates (API demo -- requires TimesFM 2.5 + timesfm[xreg] for real inference)
cd examples/covariates-forecasting && python demo_covariates.py
```

### Expected Outputs

| Example | Key output files | Acceptance criteria |
| ------- | ---------------- | ------------------- |
| global-temperature | `output/forecast_output.json`, `output/forecast_visualization.png` | `point_forecast` has 12 values; PNG shows context + forecast + PI bands |
| anomaly-detection | `output/anomaly_detection.json`, `output/anomaly_detection.png` | Sep 2023 flagged CRITICAL (z >= 3.0); >= 2 forecast CRITICAL from injected anomalies |
| covariates-forecasting | `output/sales_with_covariates.csv`, `output/covariates_data.png` | CSV has 108 rows (3 stores x 36 weeks); stores have **distinct** price arrays |

## Quality Checklist

Run this checklist after every TimesFM task before declaring success:

- [ ] **Output shape correct** -- `point_fc` shape is `(n_series, horizon)`, `quant_fc` is `(n_series, horizon, 10)`
- [ ] **Quantile indices** -- index 0 = mean, 1 = q10, 2 = q20 ... 9 = q90. **NOT** 0 = q0, 1 = q10.
- [ ] **Frequency flag** -- TimesFM 1.0/2.0: pass `freq=[0]` for monthly data. TimesFM 2.5: no freq flag.
- [ ] **Series length** -- context must be >= 32 data points (model minimum). Warn if shorter.
- [ ] **No NaN** -- `np.isnan(point_fc).any()` should be False. Check input series for gaps first.
- [ ] **Visualization axes** -- if multiple panels share data, use `sharex=True`. All time axes must cover the same span.
- [ ] **Binary outputs in Git LFS** -- PNG and GIF files must be tracked via `.gitattributes` (repo root already configured).
- [ ] **No large datasets committed** -- any real dataset > 1 MB should be downloaded to `tempfile.mkdtemp()` and annotated in code.
- [ ] **`matplotlib.use('Agg')`** -- must appear before any pyplot import when running headless.
- [ ] **`infer_is_positive`** -- set `False` for temperature anomalies, financial returns, or any series that can be negative.

## Common Mistakes

These bugs have appeared in this skill's examples. Learn from them:

1. **Quantile index off-by-one** -- The most common mistake. `quant_fc[..., 0]` is the **mean**, not q0. q10 = index 1, q90 = index 9. Always define named constants: `IDX_Q10, IDX_Q20, IDX_Q80, IDX_Q90 = 1, 2, 8, 9`.

2. **Variable shadowing in comprehensions** -- If you build per-series covariate dicts inside a loop, do NOT use the loop variable as the comprehension variable. Accumulate into separate `dict[str, ndarray]` outside the loop, then assign.
   ```python
   # WRONG -- outer `store_id` gets shadowed:
   covariates = {store_id: arr[store_id] for store_id in stores}  # inside outer loop over store_id
   # CORRECT -- use a different name or accumulate beforehand:
   prices_by_store: dict[str, np.ndarray] = {}
   for store_id, config in stores.items():
       prices_by_store[store_id] = compute_price(config)
   ```

3. **Wrong CSV column name** -- The global-temperature CSV uses `anomaly_c`, not `anomaly`. Always `print(df.columns)` before accessing.

4. **`tight_layout()` warning with `sharex=True`** -- Harmless; suppress with `plt.tight_layout(rect=[0, 0, 1, 0.97])` or ignore.

5. **TimesFM 2.5 required for `forecast_with_covariates()`** -- TimesFM 1.0 does NOT have this method. Install `uv pip install timesfm[xreg]` and use checkpoint `google/timesfm-2.5-200m-pytorch`.

6. **Future covariates must span the full horizon** -- Dynamic covariates (price, promotions, holidays) must have values for BOTH the context AND the forecast horizon. You cannot pass context-only arrays.

7. **Anomaly thresholds must be defined once** -- Define `CRITICAL_Z = 3.0`, `WARNING_Z = 2.0` as module-level constants. Never hardcode `3` or `2` inline.

8. **Context anomaly detection uses residuals, not raw values** -- Always detrend first (`np.polyfit` linear, or seasonal decomposition), then Z-score the residuals. Raw-value Z-scores are misleading on trending data.

## Validation & Verification

Use the example outputs as regression baselines. If you change forecasting logic, verify:

```bash
# Anomaly detection regression check:
python -c "
import json
d = json.load(open('examples/anomaly-detection/output/anomaly_detection.json'))
ctx = d['context_summary']
assert ctx['critical'] >= 1, 'Sep 2023 must be CRITICAL'
assert any(r['date'] == '2023-09' and r['severity'] == 'CRITICAL'
           for r in d['context_detections']), 'Sep 2023 not found'
print('Anomaly detection regression: PASS')"

# Covariates regression check:
python -c "
import pandas as pd
df = pd.read_csv('examples/covariates-forecasting/output/sales_with_covariates.csv')
assert len(df) == 108, f'Expected 108 rows, got {len(df)}'
prices = df.groupby('store_id')['price'].mean()
assert prices['store_A'] > prices['store_B'] > prices['store_C'], 'Store price ordering wrong'
print('Covariates regression: PASS')"
```
