---
title: TimeFlow Pro
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: true
app_file: app.py
sdk_version: 1.52.2
---

# 📊 TimeFlow Pro

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**Intelligent Time Series Data Analysis and Preprocessing Platform**

*Advanced pipeline for data preparation and feature engineering*

[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face%20Space-blue)](https://huggingface.co/spaces/your-username/timeflow-pro)
[![Streamlit](https://img.shields.io/badge/Interface-Streamlit-FF4B4B)](https://streamlit.io)
[![Python](https://img.shields.io/badge/Python-3.9+-blue)](https://python.org)

</div>

## 🌟 Overview

TimeFlow Pro is a comprehensive platform for time series data analysis, preprocessing, and feature engineering. Designed for data scientists and analysts, it provides an intuitive interface for transforming raw time series data into ML-ready datasets with advanced preprocessing capabilities.

## 🚀 Key Features

### 📈 **Data Analysis & Visualization**
- **Interactive Data Exploration**: Real-time preview and statistics
- **Missing Value Analysis**: Smart detection and handling strategies
- **Outlier Detection**: Multiple methods including IQR, Z-Score, Isolation Forest
- **Temporal Analysis**: Seasonality detection, trend analysis, decomposition

### ⚙️ **Advanced Preprocessing Pipeline**
- **Feature Engineering**: Automatic lag features, rolling statistics, seasonal components
- **Stationarity Checking**: ADF tests and transformation suggestions
- **Data Scaling**: Robust, Standard, MinMax, and custom scaling methods
- **Feature Selection**: Correlation, variance, mutual information, RF importance

### 🏗️ **ML-Ready Outputs**
- **Train/Validation/Test Splits**: Time-based or random splitting
- **Multiple Export Formats**: CSV, Parquet, Excel, JSON
- **Model Integration**: Ready-to-use datasets for scikit-learn, XGBoost, LightGBM
- **Visual Reports**: Comprehensive pipeline execution reports

## 🎮 Quick Start

### 1. **Upload Your Data**
- Support for CSV, Excel, Parquet formats
- Automatic date parsing and validation
- Smart column type detection

### 2. **Configure Pipeline**
```python
# Example configuration
config = {
    'target_column': 'sales',
    'test_size': 0.2,
    'max_lags': 5,
    'seasonal_period': 365,
    'scaling_method': 'robust'
}
```

### 3. **Run Pipeline & Export**
- Execute full preprocessing pipeline
- Download processed data
- Get feature importance reports
- Export modeling datasets

## 📊 Technical Architecture

### 🔧 **Pipeline Components**
```
Data Loading → Validation → Missing Handling → Outlier Treatment
     ↓
Feature Engineering → Stationarity Check → Correlation Analysis
     ↓
Data Splitting → Scaling → Feature Selection → Final Validation
```

### 🏆 **Core Features**
- **Multi-stage Validation**: Raw, processed, and final data validation
- **Memory Optimization**: Efficient handling of large datasets
- **Error Recovery**: Graceful handling of pipeline failures
- **Reproducible Results**: Configuration saving and logging

## 📚 Use Cases

### 🏢 **Business Analytics**
- Sales forecasting and trend analysis
- Inventory optimization
- Customer behavior prediction
- Financial time series analysis

### 🏭 **Industrial Applications**
- Sensor data preprocessing
- Predictive maintenance
- Quality control monitoring
- Energy consumption forecasting

### 🎓 **Academic Research**
- Time series modeling experiments
- Feature engineering research
- Algorithm comparison studies
- Educational tool for data science

## 🛠️ Installation

### Local Development
```bash
# Clone repository
git clone https://huggingface.co/spaces/your-username/timeflow-pro
cd timeflow-pro

# Install dependencies
pip install -r requirements.txt

# Run application
streamlit run app.py
```

### Docker Deployment
```bash
# Build Docker image
docker build -t timeflow-pro .

# Run container
docker run -p 8501:8501 timeflow-pro
```

## 🌐 API Usage Example

```python
from timeflow_pro import TimeFlowPipeline
import pandas as pd

# Load your data
data = pd.read_csv('your_data.csv')

# Configure pipeline
config = {
    'target_column': 'target',
    'test_size': 0.2,
    'max_lags': 7,
    'seasonal_period': 30
}

# Create and run pipeline
pipeline = TimeFlowPipeline(config)
processed_data = pipeline.run(data)

# Get modeling data
modeling_data = pipeline.get_modeling_data()
X_train, y_train = modeling_data['X_train'], modeling_data['y_train']
```

## 📈 Performance Benchmarks

| Dataset Size | Processing Time | Memory Usage | Features Generated |
|--------------|----------------|--------------|-------------------|
| 10K rows | ~5 seconds | <500 MB | 50-100 features |
| 100K rows | ~30 seconds | <1 GB | 100-200 features |
| 1M rows | ~5 minutes | <2 GB | 200-500 features |

## 🔧 Configuration Options

### **Data Processing**
- `missing_threshold`: Threshold for column removal (0.0-0.5)
- `outlier_method`: IQR, Z-Score, or Isolation Forest
- `scaling_method`: Robust, Standard, MinMax, or None

### **Feature Engineering**
- `max_lags`: Maximum lag features (1-20)
- `seasonal_period`: Seasonal window (7, 30, 90, 365)
- `rolling_windows`: List of rolling windows [7, 30, 90]

### **Model Preparation**
- `feature_selection_method`: Correlation, Variance, RF, Mutual Info
- `max_features`: Maximum features to select (5-100)
- `split_method`: Time-based or random splitting

## 📋 Requirements

### **Core Dependencies**
```txt
streamlit>=1.28.0
pandas>=2.0.0
numpy>=1.24.0
plotly>=5.17.0
scikit-learn>=1.3.0
```

### **Optional Dependencies**
```txt
xgboost>=2.0.0      # For XGBoost feature importance
lightgbm>=4.0.0     # For LightGBM integration
statsmodels>=0.14.0 # For advanced time series analysis
```

## 🤝 Contributing

We welcome contributions! Here's how you can help:

### **Areas for Contribution**
1. **New Feature Engineering Methods**
2. **Additional Visualization Types**
3. **Export Format Support**
4. **Performance Optimizations**
5. **Documentation Improvements**

### **Development Workflow**
```bash
# 1. Fork the repository
# 2. Create feature branch
git checkout -b feature/new-feature

# 3. Make changes and test
# 4. Submit pull request
```

## 📜 License

This project is licensed under the **MIT License** - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

### **Special Thanks To:**
- **Streamlit Team** for the amazing framework
- **Hugging Face** for hosting the Space
- **Open Source Community** for invaluable libraries
- **All Contributors** who helped improve TimeFlow Pro

### **Built With:**
- 🐍 Python
- 📊 Streamlit
- 🎨 Plotly
- 🔧 Scikit-learn
- 📈 Pandas & NumPy

## 📞 Support & Contact

### **Get Help:**
- 📧 **Email**: cool.araby@gmail.com
- 💬 **Issues**: [GitHub Issues](https://github.com/your-username/timeflow-pro/issues)
- 💡 **Discussions**: [Community Forum](https://github.com/your-username/timeflow-pro/discussions)

### **Stay Updated:**
- ⭐ **Star** the repository
- 👁️ **Watch** for releases
- 🔔 **Enable notifications**

---

<div align="center">

**Transform Your Time Series Data with Ease**

*TimeFlow Pro - Making Data Preparation Simple and Powerful*

[![Follow on Hugging Face](https://img.shields.io/badge/Follow%20on-🤗%20Hugging%20Face-yellow)](https://huggingface.co/your-username)
[![GitHub Stars](https://img.shields.io/github/stars/your-username/timeflow-pro?style=social)](https://github.com/your-username/timeflow-pro)

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