| # M5 Demand Forecasting Artifacts |
|
|
| ## What the models predict |
| The models predict weekly demand (sales) for 28-day horizons (d_1942 through d_1969) for 30,490 M5 series. |
|
|
| ## Exact input contract |
| - Features must be computed as-of origin day d_1913 using only data with day index <= D |
| - Categorical features must be encoded using categorical_maps.json with .map() |
| - Feature order must match feature_schema.json feature_names element by element |
| - All float columns must be float32; no float64 or object columns permitted |
| - Target encoding (dept_id, store_id, item_id) is computed only on data before the origin (leakage-safe) |
| |
| ## Files required together |
| Boosters alone are insufficient. Required files: |
| - artifacts/feature_schema.json |
| - artifacts/categorical_maps.json |
| - artifacts/training_config.json |
| - data/processed/m5_melted.parquet |
| |
| ## Known limitation |
| Predictions valid only for recorded forecast origin unless history is supplied |
| |
| ## Recorded local WRMSSE per fold |
| - Fold A: 148.327863 |
| - Fold B: 121.276794 |
| - Fold C: 167.073294 |
| - Mean: 145.559317 |
| |
| ## Copy-pasteable load instructions |
| ```python |
| import lightgbm as lgb |
| import pandas as pd |
| |
| models = {} |
| for f in os.listdir('artifacts/models/'): |
| model = lgb.Booster(model_file='artifacts/models/' + f) |
| models[f] = model |
| |
| with open('artifacts/training_config.json') as f: |
| config = json.load(f) |
| with open('artifacts/feature_schema.json') as f: |
| schema = json.load(f) |
| with open('artifacts/categorical_maps.json') as f: |
| cat_maps = json.load(f) |
| |
| df = pd.read_parquet('data/processed/m5_melted.parquet') |
| ``` |
| |
