rishini/NPN-sarimax

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