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NPN-sarimax
rishini/NPN-sarimax
NPN-sarimax is a machine learning model from rishini. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Seasonal ARIMA with eXogenous variables trained on aggregated daily M5 sales data.
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Public
Updated Aug 14, 2026
Repo size
178 MB
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Repo makeup
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.pkl88.2 MB · 100%
At a glance
- Access
- Public
- Created
- Aug 14, 2026
- Updated
- Aug 14, 2026
- SHA
- 15724dce
Model card
From the Hugging Face model README
SARIMAX Model for M5 Demand Forecasting
Overview
Seasonal ARIMA with eXogenous variables trained on aggregated daily M5 sales data.
Model Details
- Architecture: SARIMAX(2,1,1)(1,1,1,7)
- Training Data: 1,913 days with 6 exogenous features
- Test Period: 28 days (2016-04-25 to 2016-05-22)
- Exogenous Variables: wday, month, snap_CA, snap_TX, snap_WI, has_event
Performance
| Metric | Value |
|---|---|
| RMSE | 2,759.70 |
| MAE | 2,260.25 |
| MAPE | 4.98% |
| AIC | 35869.68 |
Key Features
- Captures autocorrelation and seasonal patterns
- Exogenous variables provide additional signal
- Weekly seasonality (s=7) for day-of-week effects
- Event indicators (holidays, SNAP days) improve accuracy
Usage
import pickle
import pandas as pd
with open('model.pkl', 'rb') as f:
model = pickle.load(f)
forecast = model.forecast(steps=28, exog=exog_future)
Notes
- Best performance among the three statistical models
- Exogenous variables (especially SNAP indicators) significantly improve predictions
- Larger model size (85MB) due to seasonal components
- Created
- Aug 14, 2026
- Updated
- Aug 14, 2026