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README.md

@@ -1,10 +1,86 @@
+
 ---
-title: Store Sales Ts Forecasting
-emoji: ⚡
-colorFrom: indigo
-colorTo: pink
+title: Store Sales Time Series Forecasting
+emoji: 📈
+colorFrom: blue
+colorTo: green
 sdk: docker
+sdk_version: 1
+app_file: app.py
 pinned: false
+short_description: Forecast store sales over 13 weeks using LSTM
+license: mit
+tags:
+  - time-series
+  - forecasting
+  - lstm
+  - retail
+  - pytorch
+library_name: pytorch
+model-index:
+  - name: ImprovedCashFlowLSTM
+    results:
+      - task:
+          type: time-series-forecasting
+        dataset:
+          name: Kaggle Store Sales
+          type: store-sales-time-series-forecasting
+        metrics:
+          - name: MAE
+            type: mae
+            value: 710.75
+          - name: RMSE
+            type: rmse
+            value: 1108.51
+          - name: MAPE
+            type: mape
+            value: 7.14
+          - name: R2
+            type: r2
+            value: 0.8633
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Store Sales Forecasting Model
+## Model Overview
+This is an Improved Cash Flow LSTM model for forecasting store sales over a 13-week horizon, trained on the Kaggle Store Sales dataset.
+## Model Architecture
+- **Type**: LSTM
+- **Input Size**: 20
+- **Hidden Size**: 128
+- **Number of Layers**: 2
+- **Dropout**: 0.2
+- **Forecast Horizon**: 13 weeks
+- **Parameters**: 227,085
+## Features Used
+sales, onpromotion, trend, dayofweek_sin, dayofweek_cos, month_sin, month_cos, is_weekend, quarter, sales_lag_1, sales_lag_7, sales_lag_14, sales_ma_7, sales_ma_14, sales_ratio_7, sales_ratio_14, promo_lag_1, promo_running_sum, dcoilwtico, is_holiday
+## Performance Metrics
+- **MAE**: $710.75
+- **RMSE**: $1108.51
+- **MAPE**: 7.14%
+- **R² Score**: 0.8633
+## Usage
+1. Install dependencies: `pip install -r requirements.txt`
+2. Use `inference.py` to load the model and make predictions.
+3. Input data must have 21 timesteps and match the feature set, scaled using `scaler.pkl`.
+### Example
+```python
+import numpy as np
+from inference import load_model_and_artifacts, forecast
+model, scaler, feature_names = load_model_and_artifacts()
+sequences = np.random.random((1, 21, len(feature_names)))  # Replace with actual data
+predictions, uncertainties = forecast(model, scaler, sequences)
+print("Predictions:", predictions)
+```
+## Training Details
+- **Dataset**: Kaggle Store Sales Time Series Forecasting
+- **Data Shape**: (8250, 20)
+- **Sequences**: 8217, length 21
+- **Preprocessing**: StandardScaler, conservative outlier removal
+- **Epochs**: 107 (early stopping)
+- **Optimizer**: Adam with ReduceLROnPlateau
+- **Loss**: MSE
+## Limitations
+- Predictions are sensitive to input data quality.
+- Model assumes consistent feature scaling.
+- Performance may degrade on sparse or outlier-heavy data.
+## License
+MIT License