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README.md

@@ -1,4 +1,3 @@
-
 ---
 title: Store Sales Time Series Forecasting
 emoji: 📈
@@ -31,56 +30,62 @@ model-index:
             value: 710.75
           - name: RMSE
             type: rmse
-            value: 1108.51
+            value: 1108.36
           - name: MAPE
             type: mape
-            value: 7.14
+            value: 7.16
           - name: R2
             type: r2
             value: 0.8633
 ---
-# 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.
+# Store Sales Time Series Forecasting
+
+## Overview
+This Hugging Face Space hosts an LSTM-based model (`ImprovedCashFlowLSTM`) for forecasting store sales over a 13-week horizon, trained on the Kaggle Store Sales Time Series Forecasting dataset.
+
 ## Model Architecture
 - **Type**: LSTM
-- **Input Size**: 20
+- **Input Size**: 20 features
 - **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
+- **Parameters**: 227,441
+- **Features**: 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%
+- **RMSE**: $1108.36
+- **MAPE**: 7.16%
 - **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`.
+1. **Synthetic Data**: Click "Generate Synthetic Data" to create a 21-timestep sample dataset (sales ~$3,000–19,000) and view a 13-week forecast.
+2. **Custom Data**: Upload a CSV with 21 timesteps and 20 features matching the above feature names.
+3. **Output**: View forecasts in a table and Plotly graph with uncertainty bands.
+
 ### 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)
+from inference import load_model_and_artifacts, predict
+model, scaler, feature_names, config = load_model_and_artifacts()
+sequences = scaler.transform(your_data[feature_names].values).reshape(1, 21, 20)
+predictions, uncertainties = predict(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)
+- **Epochs**: 70 (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.
+- Predictions are sensitive to input data quality and scaling.
+- Performance may degrade on low-sales stores (<$3,000) not included in training.
+- Assumes consistent feature engineering as in training.
+
 ## License
-MIT License
+MIT License