File size: 1,214 Bytes
0cbdee6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | # ARIMA Model for M5 Demand Forecasting
## Overview
AutoRegressive Integrated Moving Average trained on aggregated daily M5 sales data.
## Model Details
- **Architecture**: ARIMA(3,1,1) with trend
- **Training Data**: 1,913 days of aggregated daily sales
- **Test Period**: 28 days (2016-04-25 to 2016-05-22)
- **Differencing**: d=1 (first differencing required for stationarity)
## Stationarity Test
- ADF Statistic: -1.565373
- p-value: 0.500960
- Original data non-stationary; first differencing achieves stationarity
## Performance
| Metric | Value |
|--------|-------|
| RMSE | 6,459.70 |
| MAE | 4,852.62 |
| MAPE | 10.48% |
| AIC | 37,801.28 |
## Key Features
- AR component: 3 autoregressive terms
- I component: 1st order differencing (d=1)
- MA component: 1 moving average term
- Trend term: 't' (deterministic trend)
## Usage
```python
import pickle
with open('model.pkl', 'rb') as f:
model = pickle.load(f)
forecast = model.forecast(steps=28)
```
## Notes
- Simplest model but requires manual order selection
- No seasonal component (SARIMAX handles this better)
- Good baseline but underperforms compared to Prophet and SARIMAX
- Smallest model file (6.5MB) due to no seasonal components
|