Welcome to the Time Series Analysis repository! This is your one-stop destination to dive into the world of time series, explore open-source resources, and level up your skills using quality tools and libraries.
Time series analysis is a statistical technique used to analyze data points collected or recorded at specific time intervals. It is widely used to:
- Forecast future values (e.g., stock prices, weather).
- Identify trends and seasonal patterns.
- Detect anomalies in data.
- Understand the impact of events over time.
- Finance: Predicting stock prices or portfolio performance.
- Healthcare: Monitoring patient vitals over time.
- IoT: Analyzing sensor data from devices.
- Time Series Analysis with Statsmodels
- Practical Time Series Forecasting - GitHub
- Time-Series-Analysis-and-Forecasting-with-Python
- Time Series Analysis in Python - Comprehensive guide for Python developers.
- Awesome Time Series Analysis - Curated list of resources.
- Deep Learning for Time Series - Time series with convolutional networks.
- NumPy: For numerical computations.
- Pandas: For data manipulation and analysis.
- SciPy: Advanced scientific computing.
- Statsmodels: For statistical models and hypothesis testing.
- scikit-learn: For machine learning integration.
- Prophet: Forecasting tool by Meta.
- ARIMA: For time series modeling.
- Yahoo Finance: For fetching financial data.
- Matplotlib: Visualization library.
- Seaborn: Statistical data visualization.
We welcome contributions to make this repository better! Feel free to:
- Add more resources.
- Share your projects.
- Report issues or suggest improvements.
Have questions or want to share your progress? Contact us or open an issue!
Happy Analyzing! 🚀