Welcome to my repository containing the notes for the "Advanced Time Series" course! This repository serves as a comprehensive resource for all the concepts, techniques, and examples covered throughout the course.
In this course, we delve into advanced techniques for analyzing and forecasting time series data. We explore various models and methodologies specifically designed to handle the complexities often associated with time-dependent data. By the end of the course, you'll have a solid understanding of advanced time series concepts and be equipped with practical skills to tackle real-world time series problems.
- Introduction to time series analysis
- Time series decomposition
- Autoregressive Integrated Moving Average (ARIMA) models
- Seasonal ARIMA (SARIMA) models
- Exponential smoothing methods
- Vector Autoregression (VAR) models
- Long Short-Term Memory (LSTM) networks for time series forecasting
- Time series cross-validation techniques
- Advanced topics in time series analysis
The repository is organized into the following folders:
01-introduction: Contains notes and code examples related to the introduction to time series analysis.02-arima-models: Includes notes, exercises, and code examples for ARIMA and SARIMA models.03-exponential-smoothing: Contains notes and code examples for exponential smoothing methods.04-var-models: Includes notes and code examples for Vector Autoregression (VAR) models.05-lstm-forecasting: Contains notes and code examples for Long Short-Term Memory (LSTM) networks for time series forecasting.06-cross-validation: Includes notes and code examples for time series cross-validation techniques.07-advanced-topics: Contains additional notes and resources on advanced topics in time series analysis.
Feel free to explore each folder to access the relevant materials for each topic.
To make the most of this repository, follow these steps:
- Clone the repository to your local machine using
git clone https://github.com/your-username/advanced-time-series.git. - Navigate to the desired folder corresponding to the topic you want to study.
- Open the notes in a text editor or Jupyter Notebook to review the concepts and examples.
- Run the code examples to see the implementation in action.
- Feel free to modify the code and experiment with different parameters to deepen your understanding.
Contributions to this repository are welcome! If you find any errors, have additional resources to share, or want to improve the notes, please feel free to submit a pull request. Your contributions will help make this repository even better for the community.
This repository is licensed under the MIT License. Feel free to use the materials, share them, and modify them for your personal or educational purposes.
If you have any questions or need further assistance, please don't hesitate to reach out.
Happy learning!