README.md

metadata

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

📊 TimeFlow Pro

Intelligent Time Series Data Analysis and Preprocessing Platform

Advanced pipeline for data preparation and feature engineering

🌟 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

# 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

# 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

# Build Docker image
docker build -t timeflow-pro .

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

🌐 API Usage Example

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

streamlit>=1.28.0
pandas>=2.0.0
numpy>=1.24.0
plotly>=5.17.0
scikit-learn>=1.3.0

Optional Dependencies

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

# 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 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
• 💡 Discussions: Community Forum

Stay Updated:

• ⭐ Star the repository
• 👁️ Watch for releases
• 🔔 Enable notifications

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Transform Your Time Series Data with Ease

TimeFlow Pro - Making Data Preparation Simple and Powerful