Project Highlights

1. Time-Series Decomposition

   • I elaborate on how to interpret and use time-series decomposition to analyze data. It covers methods to extract trend, seasonal, and residual components, providing practical guidance on applying these techniques.

2. Time-Series Forecasting Versus Traditional Manufacturing

   • I provide a detailed analysis on time-series forecasting techniques versus traditional manufacturing forecasting methods.

3. Hierarchical Forecasting in Python

   • I demonstrate how to apply hierarchical forecasting methods to aggregated datasets. In my project, I explore techniques for forecasting at different levels of data aggregation, showcasing practical implementation in Python.

4. Long Short-Term Memory (LSTM) Forecasting in Python

   • I showcase a LSTM model I used for time-series forecasting tasks in Python, leveraging deep learning for improved accuracy.

5. Support Vector Regression (SVR) Forecasting in Python

   • In this project, I elaborate on the application of Support Vector Regression (SVR) for time-series forecasting. I emphasize SVR's capability to handle non-linear relationships in data, demonstrating its use in predictive modeling.