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rishini/
NPN

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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')

rishini/NPN