# ⏳ TimeCraft

Welcome to **TimeCraft**! This project was created to simplify time series analysis, database integration, and task automation.

[![MIT License](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) ![Last Commit](https://img.shields.io/github/last-commit/faelmori/timecraft) ![Repo Size](https://img.shields.io/github/repo-size/faelmori/timecraft)

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## 🚀 Key Features

* 📈 **Time Series Analysis**
  Robust scripts for modeling, forecasting, and evaluating temporal data.

* 🛢️ **Database Integration**
  Tools to efficiently connect to and query various database systems.

* ⚙️ **Automation & Notifications**
  Modules to automate data workflows and send notifications or alerts.

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## 📁 Project Structure

```
timecraft/
├── /src/                # Core logic and modules
├── /docs/               # Documentation files (README, INSTALL, CONTRIBUTING)
├── /tutorials/          # Step-by-step guides and advanced use cases
├── /data/               # Sample datasets and generated results
├── /assets/             # Visual content for outreach and publications
├── /venv/               # Virtual environment and dependency management
└── requirements.txt     # Python dependencies
```

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## 🧭 Getting Started

1. **Clone the repository**:

   ```bash
   git clone https://github.com/faelmori/timecraft.git
   cd timecraft
   ```

2. **Create and activate a virtual environment** *(optional but recommended)*:

   ```bash
   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   ```

3. **Install the dependencies**:

   ```bash
   pip install -r requirements.txt
   ```

4. **Explore the tutorials**:
   Navigate to the `/tutorials` folder for usage examples and best practices.

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## 📚 Tutorials & Examples

| Topic                                               | Description                                                  |
| --------------------------------------------------- | ------------------------------------------------------------ |
| [Time Series Forecasting](tutorials/forecasting.md) | Learn how to model and predict future data points.           |
| [Database Connection](tutorials/database.md)        | Connect to and retrieve data from supported databases.       |
| [Automation Pipeline](tutorials/automation.md)      | Build and schedule tasks using TimeCraft’s automation tools. |

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## 🤝 Contributing

Contributions of all kinds are welcome!
Please read our [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines on how to help improve TimeCraft.

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## 🛣️ Planned Features (Roadmap)

* ✅ Plug-and-play models for ARIMA, Prophet, and LSTM
* 🚧 Support for cloud-based data sources (e.g., BigQuery, Snowflake)
* 🔔 Email and webhook notification system
* 📊 Dashboard interface for visual result presentation (optional module)

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## 📄 License

This project is licensed under the [MIT License](https://opensource.org/licenses/MIT).


## 📧 Contact

If you have any questions or feedback, please feel free to reach out:

- Email: [faelmori@gmail.com](mailto:faelmori@gmail.com)
- GitHub: [faelmori/timecraft](https://github.com/faelmori/timecraft)
- LinkedIn: [Rafa Mori](https://www.linkedin.com/in/rafa-mori)