Upload prophet model with evaluation metrics

README.md

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+# Prophet Model for M5 Demand Forecasting
+
+## Overview
+Prophet (Facebook's forecasting library) trained on aggregated daily M5 sales data.
+
+## Model Details
+- **Architecture**: Additive model with trend, weekly/yearly seasonality, and event effects
+- **Training Data**: 1,913 days of aggregated daily sales (2011-01-29 to 2016-04-24)
+- **Test Period**: 28 days (2016-04-25 to 2016-05-22)
+
+## Performance
+| Metric | Value |
+|--------|-------|
+| RMSE | 4,860.67 |
+| MAE | 4,038.73 |
+| MAPE | 8.73% |
+
+## Key Features
+- Piecewise linear growth trend
+- Weekly seasonality (captures day-of-week patterns)
+- Yearly seasonality (captures seasonal trends)
+- 154 holiday/event effects
+- 95% prediction intervals
+
+## Usage
+```python
+import pickle
+import pandas as pd
+
+with open('model.pkl', 'rb') as f:
+    model = pickle.load(f)
+
+future = model.make_future_dataframe(periods=28, freq='D')
+forecast = model.predict(future)
+```
+
+## Notes
+- Prophet naturally handles missing values and structural breaks
+- The model captures strong weekly patterns (weekend vs weekday sales)
+- Lower performance than SARIMAX but faster to train

metrics.json

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+{
+  "rmse": 4860.667344419225,
+  "mae": 4038.7328999714914,
+  "mape": 8.72544270105418,
+  "method": "Prophet",
+  "train_days": 1913,
+  "test_days": 28
+}

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df52719ab0b801c14080bd001f041825bd7271fc867fbacae93442f396909b7a
+size 183616

model_config.json

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+{
+  "model_name": "Prophet",
+  "hyperparameters": {
+    "changepoint_prior_scale": 0.001,
+    "seasonality_prior_scale": 10.0,
+    "holidays_prior_scale": 10.0,
+    "interval_width": 0.95,
+    "yearly_seasonality": true,
+    "weekly_seasonality": true,
+    "daily_seasonality": false
+  },
+  "training_data": {
+    "train_days": 1913,
+    "test_days": 28,
+    "train_start": "2011-01-29 00:00:00",
+    "train_end": "2016-04-24 00:00:00"
+  },
+  "metrics": {
+    "rmse": 4860.667344419225,
+    "mae": 4038.7328999714914,
+    "mape": 8.72544270105418,
+    "method": "Prophet",
+    "train_days": 1913,
+    "test_days": 28
+  }
+}

predictions.csv

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+ds,yhat,yhat_lower,yhat_upper,actual
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