---
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: 1108.36
          - name: MAPE
            type: mape
            value: 7.16
          - name: R2
            type: r2
            value: 0.8633
---
# 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 features
- **Hidden Size**: 128
- **Number of Layers**: 2
- **Dropout**: 0.2
- **Forecast Horizon**: 13 weeks
- **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**: $1108.36
- **MAPE**: 7.16%
- **R² Score**: 0.8633

## Usage
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
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**: 70 (early stopping)
- **Optimizer**: Adam with ReduceLROnPlateau
- **Loss**: MSE

## Limitations
- 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